{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import model_selection\n",
    "from tensorflow.keras import Model\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.layers import LSTM, Dense, Dropout, Input, Flatten\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>A1</th>\n",
       "      <th>Ea1</th>\n",
       "      <th>A2</th>\n",
       "      <th>Ea2</th>\n",
       "      <th>A3</th>\n",
       "      <th>Ea3</th>\n",
       "      <th>A4</th>\n",
       "      <th>Ea4</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "      <th>Fast_rxn1</th>\n",
       "      <th>Medium_rxn1</th>\n",
       "      <th>Slow_rxn1</th>\n",
       "      <th>Fast_rxn2</th>\n",
       "      <th>Medium_rxn2</th>\n",
       "      <th>Slow_rxn2</th>\n",
       "      <th>Reaction_order</th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>1933581</td>\n",
       "      <td>273</td>\n",
       "      <td>261.472219</td>\n",
       "      <td>194</td>\n",
       "      <td>0.044413</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.236088e-02</td>\n",
       "      <td>3.977472e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>1933582</td>\n",
       "      <td>273</td>\n",
       "      <td>596.074861</td>\n",
       "      <td>62</td>\n",
       "      <td>8.957436</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.985276e-06</td>\n",
       "      <td>7.871450e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>1933583</td>\n",
       "      <td>273</td>\n",
       "      <td>423.789150</td>\n",
       "      <td>55</td>\n",
       "      <td>904.812139</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.042629e-07</td>\n",
       "      <td>7.955867e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>1933584</td>\n",
       "      <td>273</td>\n",
       "      <td>0.014179</td>\n",
       "      <td>123</td>\n",
       "      <td>0.061371</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.618686e-03</td>\n",
       "      <td>8.963359e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>1933585</td>\n",
       "      <td>273</td>\n",
       "      <td>0.045442</td>\n",
       "      <td>179</td>\n",
       "      <td>1.544173</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.689157e-03</td>\n",
       "      <td>2.451002e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 258 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Temperature          A1  Ea1          A2  Ea2  A3  Ea3  \\\n",
       "1933581     1933581          273  261.472219  194    0.044413  141   0    0   \n",
       "1933582     1933582          273  596.074861   62    8.957436    9   0    0   \n",
       "1933583     1933583          273  423.789150   55  904.812139  184   0    0   \n",
       "1933584     1933584          273    0.014179  123    0.061371   74   0    0   \n",
       "1933585     1933585          273    0.045442  179    1.544173  121   0    0   \n",
       "\n",
       "         A4  Ea4  ...  cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  \\\n",
       "1933581   0    0  ...  4.236088e-02  3.977472e-02          1            0   \n",
       "1933582   0    0  ...  9.985276e-06  7.871450e-06          1            0   \n",
       "1933583   0    0  ...  1.042629e-07  7.955867e-08          1            0   \n",
       "1933584   0    0  ...  8.618686e-03  8.963359e-03          0            0   \n",
       "1933585   0    0  ...  2.689157e-03  2.451002e-03          0            0   \n",
       "\n",
       "         Slow_rxn1  Fast_rxn2  Medium_rxn2  Slow_rxn2  \\\n",
       "1933581          0          0            0          1   \n",
       "1933582          0          0            1          0   \n",
       "1933583          0          1            0          0   \n",
       "1933584          1          0            0          1   \n",
       "1933585          1          0            1          0   \n",
       "\n",
       "                                            Reaction_order  Mechanism  \n",
       "1933581  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933582  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933583  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933584  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933585  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "\n",
       "[5 rows x 258 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv('../two_reactions_022624.csv')\n",
    "df_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_raw_df = df_raw.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "unique_values = y_raw_df['Mechanism'].unique()\n",
    "print(len(unique_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_raw.copy()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "conc_list = [col for col in df.columns if col.endswith('s')]\n",
    "# conc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299992</td>\n",
       "      <td>0.299837</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>0.299514</td>\n",
       "      <td>0.299035</td>\n",
       "      <td>0.298564</td>\n",
       "      <td>0.298101</td>\n",
       "      <td>0.297645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006314</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.007860</td>\n",
       "      <td>0.007791</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.006560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299978</td>\n",
       "      <td>0.299957</td>\n",
       "      <td>0.299935</td>\n",
       "      <td>0.299870</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>0.299740</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299996</td>\n",
       "      <td>0.299926</td>\n",
       "      <td>0.299853</td>\n",
       "      <td>0.299780</td>\n",
       "      <td>0.299561</td>\n",
       "      <td>0.299344</td>\n",
       "      <td>0.299129</td>\n",
       "      <td>0.298914</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299989</td>\n",
       "      <td>0.299887</td>\n",
       "      <td>0.297811</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.293877</td>\n",
       "      <td>0.288868</td>\n",
       "      <td>0.284657</td>\n",
       "      <td>0.281052</td>\n",
       "      <td>0.277897</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017600</td>\n",
       "      <td>0.016738</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0.014708</td>\n",
       "      <td>0.013685</td>\n",
       "      <td>0.012807</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>0.009851</td>\n",
       "      <td>0.008435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299941</td>\n",
       "      <td>0.299417</td>\n",
       "      <td>0.290150</td>\n",
       "      <td>0.282960</td>\n",
       "      <td>0.277360</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>0.257948</td>\n",
       "      <td>0.252221</td>\n",
       "      <td>0.247715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0     0.3  0.299999  0.299992  0.299837  0.299675  0.299514  0.299035   \n",
       "1     0.3  0.300000  0.299999  0.299978  0.299957  0.299935  0.299870   \n",
       "2     0.3  0.300000  0.299996  0.299926  0.299853  0.299780  0.299561   \n",
       "3     0.3  0.299989  0.299887  0.297811  0.295775  0.293877  0.288868   \n",
       "4     0.3  0.299941  0.299417  0.290150  0.282960  0.277360  0.265672   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  cINT1_3600s  \\\n",
       "0  0.298564  0.298101  0.297645  ...     0.006314     0.006981     0.007418   \n",
       "1  0.299805  0.299740  0.299675  ...     0.000503     0.000512     0.000509   \n",
       "2  0.299344  0.299129  0.298914  ...     0.000119     0.000117     0.000115   \n",
       "3  0.284657  0.281052  0.277897  ...     0.017600     0.016738     0.015892   \n",
       "4  0.257948  0.252221  0.247715  ...     0.004310     0.003784     0.003389   \n",
       "\n",
       "   cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  \\\n",
       "0     0.007767     0.007898     0.007860     0.007791     0.007491   \n",
       "1     0.000498     0.000495     0.000490     0.000485     0.000474   \n",
       "2     0.000111     0.000108     0.000106     0.000103     0.000099   \n",
       "3     0.014708     0.013685     0.012807     0.012051     0.010814   \n",
       "4     0.002952     0.002632     0.002379     0.002182     0.001874   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  \n",
       "0      0.007172      0.006560  \n",
       "1      0.000466      0.000450  \n",
       "2      0.000095      0.000088  \n",
       "3      0.009851      0.008435  \n",
       "4      0.001653      0.001348  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw = df[conc_list]\n",
    "x_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a MinMaxScaler instance\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Transpose the DataFrame so that rows become columns for scaling\n",
    "x_df_transposed = x_df_raw.T\n",
    "\n",
    "# Scale the entire transposed DataFrame\n",
    "scaled_data = scaler.fit_transform(x_df_transposed)\n",
    "\n",
    "# Transpose the scaled data back to the original orientation\n",
    "x = pd.DataFrame(scaled_data.T, columns=x_df_raw.columns, index=x_df_raw.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333324</td>\n",
       "      <td>0.333152</td>\n",
       "      <td>0.332972</td>\n",
       "      <td>0.332793</td>\n",
       "      <td>0.332261</td>\n",
       "      <td>0.331738</td>\n",
       "      <td>0.331224</td>\n",
       "      <td>0.330717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.007757</td>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.008630</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.008657</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.007289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333309</td>\n",
       "      <td>0.333285</td>\n",
       "      <td>0.333261</td>\n",
       "      <td>0.333189</td>\n",
       "      <td>0.333117</td>\n",
       "      <td>0.333045</td>\n",
       "      <td>0.332973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333329</td>\n",
       "      <td>0.333252</td>\n",
       "      <td>0.333170</td>\n",
       "      <td>0.333089</td>\n",
       "      <td>0.332846</td>\n",
       "      <td>0.332605</td>\n",
       "      <td>0.332365</td>\n",
       "      <td>0.332127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333321</td>\n",
       "      <td>0.333207</td>\n",
       "      <td>0.330901</td>\n",
       "      <td>0.328639</td>\n",
       "      <td>0.326530</td>\n",
       "      <td>0.320964</td>\n",
       "      <td>0.316285</td>\n",
       "      <td>0.312280</td>\n",
       "      <td>0.308774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019555</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.017658</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>0.014230</td>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.012016</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.009372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333268</td>\n",
       "      <td>0.332686</td>\n",
       "      <td>0.322389</td>\n",
       "      <td>0.314400</td>\n",
       "      <td>0.308178</td>\n",
       "      <td>0.295191</td>\n",
       "      <td>0.286609</td>\n",
       "      <td>0.280245</td>\n",
       "      <td>0.275239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.002424</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.001498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0  0.333333  0.333332  0.333324  0.333152  0.332972  0.332793  0.332261   \n",
       "1  0.333333  0.333333  0.333332  0.333309  0.333285  0.333261  0.333189   \n",
       "2  0.333333  0.333333  0.333329  0.333252  0.333170  0.333089  0.332846   \n",
       "3  0.333333  0.333321  0.333207  0.330901  0.328639  0.326530  0.320964   \n",
       "4  0.333333  0.333268  0.332686  0.322389  0.314400  0.308178  0.295191   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  cINT1_3600s  \\\n",
       "0  0.331738  0.331224  0.330717  ...     0.007016     0.007757     0.008242   \n",
       "1  0.333117  0.333045  0.332973  ...     0.000559     0.000569     0.000566   \n",
       "2  0.332605  0.332365  0.332127  ...     0.000133     0.000130     0.000127   \n",
       "3  0.316285  0.312280  0.308774  ...     0.019555     0.018597     0.017658   \n",
       "4  0.286609  0.280245  0.275239  ...     0.004789     0.004205     0.003766   \n",
       "\n",
       "   cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  \\\n",
       "0     0.008630     0.008775     0.008734     0.008657     0.008323   \n",
       "1     0.000553     0.000549     0.000545     0.000539     0.000527   \n",
       "2     0.000124     0.000120     0.000117     0.000115     0.000110   \n",
       "3     0.016342     0.015205     0.014230     0.013390     0.012016   \n",
       "4     0.003280     0.002925     0.002643     0.002424     0.002082   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  \n",
       "0      0.007969      0.007289  \n",
       "1      0.000517      0.000500  \n",
       "2      0.000105      0.000098  \n",
       "3      0.010945      0.009372  \n",
       "4      0.001837      0.001498  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_df = df.iloc[:, -1:]\n",
    "# y_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>3</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1933586 rows × 456 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism_(0, 146)  Mechanism_(0, 147)  Mechanism_(0, 154)  \\\n",
       "0                     False               False               False   \n",
       "1                     False               False               False   \n",
       "2                     False               False               False   \n",
       "3                     False               False               False   \n",
       "4                     False               False               False   \n",
       "...                     ...                 ...                 ...   \n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(0, 155)  Mechanism_(1, 146)  Mechanism_(1, 147)  \\\n",
       "0                     False               False               False   \n",
       "1                     False               False               False   \n",
       "2                     False               False               False   \n",
       "3                     False               False               False   \n",
       "4                     False               False               False   \n",
       "...                     ...                 ...                 ...   \n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(1, 154)  Mechanism_(1, 155)  Mechanism_(101, 126)  \\\n",
       "0                     False               False                 False   \n",
       "1                     False               False                 False   \n",
       "2                     False               False                 False   \n",
       "3                     False               False                 False   \n",
       "4                     False               False                 False   \n",
       "...                     ...                 ...                   ...   \n",
       "1933581               False               False                 False   \n",
       "1933582               False               False                 False   \n",
       "1933583               False               False                 False   \n",
       "1933584               False               False                 False   \n",
       "1933585               False               False                 False   \n",
       "\n",
       "         Mechanism_(101, 162)  ...  Mechanism_(97, 128)  Mechanism_(97, 166)  \\\n",
       "0                       False  ...                False                False   \n",
       "1                       False  ...                False                False   \n",
       "2                       False  ...                False                False   \n",
       "3                       False  ...                False                False   \n",
       "4                       False  ...                False                False   \n",
       "...                       ...  ...                  ...                  ...   \n",
       "1933581                 False  ...                False                False   \n",
       "1933582                 False  ...                False                False   \n",
       "1933583                 False  ...                False                False   \n",
       "1933584                 False  ...                False                False   \n",
       "1933585                 False  ...                False                False   \n",
       "\n",
       "         Mechanism_(97, 167)  Mechanism_(97, 181)  Mechanism_(97, 220)  \\\n",
       "0                      False                False                False   \n",
       "1                      False                False                False   \n",
       "2                      False                False                False   \n",
       "3                      False                False                False   \n",
       "4                      False                False                False   \n",
       "...                      ...                  ...                  ...   \n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 221)  Mechanism_(97, 338)  Mechanism_(97, 386)  \\\n",
       "0                      False                False                False   \n",
       "1                      False                False                False   \n",
       "2                      False                False                False   \n",
       "3                      False                False                False   \n",
       "4                      False                False                False   \n",
       "...                      ...                  ...                  ...   \n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 404)  Mechanism_(97, 452)  \n",
       "0                      False                False  \n",
       "1                      False                False  \n",
       "2                      False                False  \n",
       "3                      False                False  \n",
       "4                      False                False  \n",
       "...                      ...                  ...  \n",
       "1933581                False                False  \n",
       "1933582                False                False  \n",
       "1933583                False                False  \n",
       "1933584                False                False  \n",
       "1933585                False                False  \n",
       "\n",
       "[1933586 rows x 456 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df_oh = pd.get_dummies(y_df, columns = ['Mechanism']) \n",
    "y_df_oh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'y_df_oh_NN.h5'\n",
    "y_df_oh.to_hdf(file_path, key='data', mode='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. LSTM Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333324</td>\n",
       "      <td>0.333152</td>\n",
       "      <td>0.332972</td>\n",
       "      <td>0.332793</td>\n",
       "      <td>0.332261</td>\n",
       "      <td>0.331738</td>\n",
       "      <td>0.331224</td>\n",
       "      <td>0.330717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.007757</td>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.008630</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.008657</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.007289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333309</td>\n",
       "      <td>0.333285</td>\n",
       "      <td>0.333261</td>\n",
       "      <td>0.333189</td>\n",
       "      <td>0.333117</td>\n",
       "      <td>0.333045</td>\n",
       "      <td>0.332973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333329</td>\n",
       "      <td>0.333252</td>\n",
       "      <td>0.333170</td>\n",
       "      <td>0.333089</td>\n",
       "      <td>0.332846</td>\n",
       "      <td>0.332605</td>\n",
       "      <td>0.332365</td>\n",
       "      <td>0.332127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333321</td>\n",
       "      <td>0.333207</td>\n",
       "      <td>0.330901</td>\n",
       "      <td>0.328639</td>\n",
       "      <td>0.326530</td>\n",
       "      <td>0.320964</td>\n",
       "      <td>0.316285</td>\n",
       "      <td>0.312280</td>\n",
       "      <td>0.308774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019555</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.017658</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>0.014230</td>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.012016</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.009372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333268</td>\n",
       "      <td>0.332686</td>\n",
       "      <td>0.322389</td>\n",
       "      <td>0.314400</td>\n",
       "      <td>0.308178</td>\n",
       "      <td>0.295191</td>\n",
       "      <td>0.286609</td>\n",
       "      <td>0.280245</td>\n",
       "      <td>0.275239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.002424</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.001498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0  0.333333  0.333332  0.333324  0.333152  0.332972  0.332793  0.332261   \n",
       "1  0.333333  0.333333  0.333332  0.333309  0.333285  0.333261  0.333189   \n",
       "2  0.333333  0.333333  0.333329  0.333252  0.333170  0.333089  0.332846   \n",
       "3  0.333333  0.333321  0.333207  0.330901  0.328639  0.326530  0.320964   \n",
       "4  0.333333  0.333268  0.332686  0.322389  0.314400  0.308178  0.295191   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  cINT1_3600s  \\\n",
       "0  0.331738  0.331224  0.330717  ...     0.007016     0.007757     0.008242   \n",
       "1  0.333117  0.333045  0.332973  ...     0.000559     0.000569     0.000566   \n",
       "2  0.332605  0.332365  0.332127  ...     0.000133     0.000130     0.000127   \n",
       "3  0.316285  0.312280  0.308774  ...     0.019555     0.018597     0.017658   \n",
       "4  0.286609  0.280245  0.275239  ...     0.004789     0.004205     0.003766   \n",
       "\n",
       "   cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  \\\n",
       "0     0.008630     0.008775     0.008734     0.008657     0.008323   \n",
       "1     0.000553     0.000549     0.000545     0.000539     0.000527   \n",
       "2     0.000124     0.000120     0.000117     0.000115     0.000110   \n",
       "3     0.016342     0.015205     0.014230     0.013390     0.012016   \n",
       "4     0.003280     0.002925     0.002643     0.002424     0.002082   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  \n",
       "0      0.007969      0.007289  \n",
       "1      0.000517      0.000500  \n",
       "2      0.000105      0.000098  \n",
       "3      0.010945      0.009372  \n",
       "4      0.001837      0.001498  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Mechanism_(0, 146)</th>\n",
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       "      <th>Mechanism_(101, 162)</th>\n",
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       "      <th>Mechanism_(97, 166)</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 456 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mechanism_(0, 146)  Mechanism_(0, 147)  Mechanism_(0, 154)  \\\n",
       "0               False               False               False   \n",
       "1               False               False               False   \n",
       "2               False               False               False   \n",
       "3               False               False               False   \n",
       "4               False               False               False   \n",
       "\n",
       "   Mechanism_(0, 155)  Mechanism_(1, 146)  Mechanism_(1, 147)  \\\n",
       "0               False               False               False   \n",
       "1               False               False               False   \n",
       "2               False               False               False   \n",
       "3               False               False               False   \n",
       "4               False               False               False   \n",
       "\n",
       "   Mechanism_(1, 154)  Mechanism_(1, 155)  Mechanism_(101, 126)  \\\n",
       "0               False               False                 False   \n",
       "1               False               False                 False   \n",
       "2               False               False                 False   \n",
       "3               False               False                 False   \n",
       "4               False               False                 False   \n",
       "\n",
       "   Mechanism_(101, 162)  ...  Mechanism_(97, 128)  Mechanism_(97, 166)  \\\n",
       "0                 False  ...                False                False   \n",
       "1                 False  ...                False                False   \n",
       "2                 False  ...                False                False   \n",
       "3                 False  ...                False                False   \n",
       "4                 False  ...                False                False   \n",
       "\n",
       "   Mechanism_(97, 167)  Mechanism_(97, 181)  Mechanism_(97, 220)  \\\n",
       "0                False                False                False   \n",
       "1                False                False                False   \n",
       "2                False                False                False   \n",
       "3                False                False                False   \n",
       "4                False                False                False   \n",
       "\n",
       "   Mechanism_(97, 221)  Mechanism_(97, 338)  Mechanism_(97, 386)  \\\n",
       "0                False                False                False   \n",
       "1                False                False                False   \n",
       "2                False                False                False   \n",
       "3                False                False                False   \n",
       "4                False                False                False   \n",
       "\n",
       "   Mechanism_(97, 404)  Mechanism_(97, 452)  \n",
       "0                False                False  \n",
       "1                False                False  \n",
       "2                False                False  \n",
       "3                False                False  \n",
       "4                False                False  \n",
       "\n",
       "[5 rows x 456 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df = y_df_oh.copy()\n",
    "y_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 456)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.where(y_df, 1, 0)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_df, x_val_df, y_train, y_val = model_selection.train_test_split(x, y, test_size=0.05, random_state=37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size:   (1836906, 240) (1836906, 456)\n",
      "Validation set size: (96680, 240) (96680, 456)\n"
     ]
    }
   ],
   "source": [
    "print('Training set size:  ',x_train_df.shape, y_train.shape)\n",
    "print('Validation set size:',x_val_df.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1459100</th>\n",
       "      <td>0.666666</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.561708</td>\n",
       "      <td>0.553785</td>\n",
       "      <td>0.547734</td>\n",
       "      <td>0.540914</td>\n",
       "      <td>0.535847</td>\n",
       "      <td>0.531923</td>\n",
       "      <td>0.528790</td>\n",
       "      <td>0.524104</td>\n",
       "      <td>0.520750</td>\n",
       "      <td>0.516260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896046</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833330</td>\n",
       "      <td>0.833298</td>\n",
       "      <td>0.832626</td>\n",
       "      <td>0.831924</td>\n",
       "      <td>0.831228</td>\n",
       "      <td>0.829172</td>\n",
       "      <td>0.827164</td>\n",
       "      <td>0.825202</td>\n",
       "      <td>0.823285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968160</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333297</td>\n",
       "      <td>0.332970</td>\n",
       "      <td>0.326250</td>\n",
       "      <td>0.319520</td>\n",
       "      <td>0.313114</td>\n",
       "      <td>0.295610</td>\n",
       "      <td>0.280275</td>\n",
       "      <td>0.266704</td>\n",
       "      <td>0.254590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069588</td>\n",
       "      <td>0.063861</td>\n",
       "      <td>0.058443</td>\n",
       "      <td>0.051457</td>\n",
       "      <td>0.045791</td>\n",
       "      <td>0.041188</td>\n",
       "      <td>0.037404</td>\n",
       "      <td>0.031587</td>\n",
       "      <td>0.027340</td>\n",
       "      <td>0.021568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605448</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
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       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
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       "    <tr>\n",
       "      <th>1574931</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666148</td>\n",
       "      <td>0.661630</td>\n",
       "      <td>0.603996</td>\n",
       "      <td>0.578012</td>\n",
       "      <td>0.563949</td>\n",
       "      <td>0.544214</td>\n",
       "      <td>0.535572</td>\n",
       "      <td>0.530625</td>\n",
       "      <td>0.527411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065278</td>\n",
       "      <td>0.065304</td>\n",
       "      <td>0.065319</td>\n",
       "      <td>0.065338</td>\n",
       "      <td>0.065347</td>\n",
       "      <td>0.065355</td>\n",
       "      <td>0.065362</td>\n",
       "      <td>0.065369</td>\n",
       "      <td>0.065375</td>\n",
       "      <td>0.065381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "1459100  0.666666  0.000009  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.833333  0.833330  0.833298  0.832626  0.831924  0.831228  0.829172   \n",
       "968160   0.333333  0.333297  0.332970  0.326250  0.319520  0.313114  0.295610   \n",
       "605448   0.333333  0.003058  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.666667  0.666148  0.661630  0.603996  0.578012  0.563949  0.544214   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  \\\n",
       "1459100  0.000000  0.000000  0.000000  ...     0.561708     0.553785   \n",
       "1896046  0.827164  0.825202  0.823285  ...     0.000009     0.000009   \n",
       "968160   0.280275  0.266704  0.254590  ...     0.069588     0.063861   \n",
       "605448   0.000000  0.000000  0.000000  ...     0.000006     0.000006   \n",
       "1574931  0.535572  0.530625  0.527411  ...     0.065278     0.065304   \n",
       "\n",
       "         cINT1_3600s  cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  \\\n",
       "1459100     0.547734     0.540914     0.535847     0.531923     0.528790   \n",
       "1896046     0.000008     0.000008     0.000008     0.000008     0.000008   \n",
       "968160      0.058443     0.051457     0.045791     0.041188     0.037404   \n",
       "605448      0.000004     0.000003     0.000003     0.000003     0.000002   \n",
       "1574931     0.065319     0.065338     0.065347     0.065355     0.065362   \n",
       "\n",
       "         cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "1459100     0.524104      0.520750      0.516260  \n",
       "1896046     0.000007      0.000007      0.000007  \n",
       "968160      0.031587      0.027340      0.021568  \n",
       "605448      0.000002      0.000001      0.000001  \n",
       "1574931     0.065369      0.065375      0.065381  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_species = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reshape_x(x_df_raw):\n",
    "    data = x_df_raw.values\n",
    "    data_2d = np.array(data)\n",
    "    # data_2d\n",
    "    # 12 species and 30 time points. 29 points for derivatives\n",
    "    time_points = 30\n",
    "    num_curves = number_of_species # number of species\n",
    "    num_experiments = len(data)\n",
    "\n",
    "\n",
    "    # Reshape the data into a 3D array\n",
    "    # data_3d = data.reshape(num_experiments, -1, time_points)\n",
    "    data_3d = data.reshape(num_experiments, num_curves, time_points)\n",
    "    data_3d\n",
    "    x = data_3d.swapaxes(1,2)\n",
    "    # x = reshaped_data_3d.tolist()\n",
    "    # x[0]\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1836906, 240)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = reshape_x(x_train_df)\n",
    "x_val = reshape_x(x_val_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size:   (1836906, 30, 8) (1836906, 456)\n",
      "Validation set size: (96680, 30, 8) (96680, 456)\n"
     ]
    }
   ],
   "source": [
    "print('Training set size:  ',x_train.shape, y_train.shape)\n",
    "print('Validation set size:',x_val.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def create_model_lstm_with_dropout(input_shape, output_shape):\n",
    "    kinetics = Input(shape=input_shape)\n",
    "\n",
    "    # LSTM layer\n",
    "    lstm1 = LSTM(units=128, return_sequences=True)(kinetics)\n",
    "    dropout1 = Dropout(0.2)(lstm1)\n",
    "\n",
    "    # LSTM layer\n",
    "    lstm2 = LSTM(units=128)(dropout1)\n",
    "    dropout2 = Dropout(0.2)(lstm2)\n",
    "\n",
    "    # Flatten layer\n",
    "    flat = Flatten()(dropout2)\n",
    "\n",
    "    # Output layer\n",
    "    pred = Dense(output_shape[1], activation='softmax', name='Dense_5')(flat)\n",
    "\n",
    "    model = Model(inputs=kinetics, outputs=pred)\n",
    "\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "(1836906, 456)\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "print(x_train[20][0].shape[-1])\n",
    "# print(x_train)\n",
    "print(y_train.shape)\n",
    "print(len(x_train[20]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Train LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4325 - accuracy: 0.2726\n",
      "Epoch 1: val_accuracy improved from -inf to 0.39800, saving model to best_model_weights_NN_LSTM+Dropout.h5\n",
      "3588/3588 [==============================] - 929s 258ms/step - loss: 2.4325 - accuracy: 0.2726 - val_loss: 1.6219 - val_accuracy: 0.3980\n",
      "Epoch 2/3000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\I0512620\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3588/3588 [==============================] - ETA: 0s - loss: 1.4110 - accuracy: 0.4396\n",
      "Epoch 2: val_accuracy improved from 0.39800 to 0.46951, saving model to best_model_weights_NN_LSTM+Dropout.h5\n",
      "3588/3588 [==============================] - 949s 264ms/step - loss: 1.4110 - accuracy: 0.4396 - val_loss: 1.2585 - val_accuracy: 0.4695\n",
      "Epoch 3/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.2489 - accuracy: 0.4733\n",
      "Epoch 3: val_accuracy improved from 0.46951 to 0.50458, saving model to best_model_weights_NN_LSTM+Dropout.h5\n",
      "3588/3588 [==============================] - 1145s 319ms/step - loss: 1.2489 - accuracy: 0.4733 - val_loss: 1.1249 - val_accuracy: 0.5046\n",
      "Epoch 4/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5966 - accuracy: 0.4030\n",
      "Epoch 4: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1003s 279ms/step - loss: 1.5966 - accuracy: 0.4030 - val_loss: 1.2671 - val_accuracy: 0.4672\n",
      "Epoch 5/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5328 - accuracy: 0.4224\n",
      "Epoch 5: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1219s 340ms/step - loss: 1.5328 - accuracy: 0.4224 - val_loss: 1.2256 - val_accuracy: 0.4782\n",
      "Epoch 6/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6426 - accuracy: 0.3985\n",
      "Epoch 6: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1069s 298ms/step - loss: 1.6426 - accuracy: 0.3985 - val_loss: 1.1366 - val_accuracy: 0.4987\n",
      "Epoch 7/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8158 - accuracy: 0.3898\n",
      "Epoch 7: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1022s 285ms/step - loss: 1.8158 - accuracy: 0.3898 - val_loss: 2.8854 - val_accuracy: 0.1819\n",
      "Epoch 8/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.5403 - accuracy: 0.1271\n",
      "Epoch 8: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1017s 283ms/step - loss: 3.5403 - accuracy: 0.1271 - val_loss: 3.3333 - val_accuracy: 0.1404\n",
      "Epoch 9/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3148 - accuracy: 0.1421\n",
      "Epoch 9: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1003s 280ms/step - loss: 3.3148 - accuracy: 0.1421 - val_loss: 3.9731 - val_accuracy: 0.0881\n",
      "Epoch 10/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.6515 - accuracy: 0.1114\n",
      "Epoch 10: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1084s 302ms/step - loss: 3.6515 - accuracy: 0.1114 - val_loss: 3.7354 - val_accuracy: 0.1122\n",
      "Epoch 11/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8270 - accuracy: 0.1835\n",
      "Epoch 11: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 986s 275ms/step - loss: 2.8270 - accuracy: 0.1835 - val_loss: 3.6315 - val_accuracy: 0.1316\n",
      "Epoch 12/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4805 - accuracy: 0.2220\n",
      "Epoch 12: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1075s 300ms/step - loss: 2.4805 - accuracy: 0.2220 - val_loss: 2.4853 - val_accuracy: 0.2273\n",
      "Epoch 13/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2057 - accuracy: 0.2629\n",
      "Epoch 13: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1014s 283ms/step - loss: 2.2057 - accuracy: 0.2629 - val_loss: 2.2418 - val_accuracy: 0.2695\n",
      "Epoch 14/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0060 - accuracy: 0.3006\n",
      "Epoch 14: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1018s 284ms/step - loss: 2.0060 - accuracy: 0.3006 - val_loss: 2.0028 - val_accuracy: 0.3107\n",
      "Epoch 15/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4906 - accuracy: 0.2423\n",
      "Epoch 15: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1064s 297ms/step - loss: 2.4906 - accuracy: 0.2423 - val_loss: 2.1646 - val_accuracy: 0.2710\n",
      "Epoch 16/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2090 - accuracy: 0.2755\n",
      "Epoch 16: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1078s 301ms/step - loss: 2.2090 - accuracy: 0.2755 - val_loss: 3.8036 - val_accuracy: 0.0909\n",
      "Epoch 17/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.2298 - accuracy: 0.1368\n",
      "Epoch 17: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1033s 288ms/step - loss: 4.2298 - accuracy: 0.1368 - val_loss: 4.4251 - val_accuracy: 0.0716\n",
      "Epoch 18/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3038 - accuracy: 0.1338\n",
      "Epoch 18: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 986s 275ms/step - loss: 3.3038 - accuracy: 0.1338 - val_loss: 3.5060 - val_accuracy: 0.1294\n",
      "Epoch 19/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.7663 - accuracy: 0.1852\n",
      "Epoch 19: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1008s 281ms/step - loss: 2.7663 - accuracy: 0.1852 - val_loss: 2.8304 - val_accuracy: 0.1845\n",
      "Epoch 20/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.4055 - accuracy: 0.1368\n",
      "Epoch 20: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1101s 307ms/step - loss: 3.4055 - accuracy: 0.1368 - val_loss: 4.4883 - val_accuracy: 0.0865\n",
      "Epoch 21/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8702 - accuracy: 0.1712\n",
      "Epoch 21: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1003s 280ms/step - loss: 2.8702 - accuracy: 0.1712 - val_loss: 2.8352 - val_accuracy: 0.1885\n",
      "Epoch 22/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4733 - accuracy: 0.2194\n",
      "Epoch 22: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1106s 308ms/step - loss: 2.4733 - accuracy: 0.2194 - val_loss: 2.5702 - val_accuracy: 0.2225\n",
      "Epoch 23/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2940 - accuracy: 0.2453\n",
      "Epoch 23: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1121s 312ms/step - loss: 2.2940 - accuracy: 0.2453 - val_loss: 2.3385 - val_accuracy: 0.2413\n",
      "Epoch 24/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1657 - accuracy: 0.2670\n",
      "Epoch 24: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1013s 282ms/step - loss: 2.1657 - accuracy: 0.2670 - val_loss: 2.6301 - val_accuracy: 0.2358\n",
      "Epoch 25/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3374 - accuracy: 0.2558\n",
      "Epoch 25: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1139s 318ms/step - loss: 2.3374 - accuracy: 0.2558 - val_loss: 4.5693 - val_accuracy: 0.0984\n",
      "Epoch 26/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6892 - accuracy: 0.1965\n",
      "Epoch 26: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1065s 297ms/step - loss: 2.6892 - accuracy: 0.1965 - val_loss: 5.2034 - val_accuracy: 0.0946\n",
      "Epoch 27/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3412 - accuracy: 0.1307\n",
      "Epoch 27: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1119s 312ms/step - loss: 3.3412 - accuracy: 0.1307 - val_loss: 4.2253 - val_accuracy: 0.1038\n",
      "Epoch 28/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6061 - accuracy: 0.2061\n",
      "Epoch 28: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1010s 282ms/step - loss: 2.6061 - accuracy: 0.2061 - val_loss: 5.4343 - val_accuracy: 0.1669\n",
      "Epoch 29/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2817 - accuracy: 0.2490\n",
      "Epoch 29: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1001s 279ms/step - loss: 2.2817 - accuracy: 0.2490 - val_loss: 6.1682 - val_accuracy: 0.1946\n",
      "Epoch 30/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1544 - accuracy: 0.2707\n",
      "Epoch 30: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1021s 285ms/step - loss: 2.1544 - accuracy: 0.2707 - val_loss: 2.9711 - val_accuracy: 0.2273\n",
      "Epoch 31/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0755 - accuracy: 0.2869\n",
      "Epoch 31: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1052s 293ms/step - loss: 2.0755 - accuracy: 0.2869 - val_loss: 2.7752 - val_accuracy: 0.2330\n",
      "Epoch 32/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0086 - accuracy: 0.3017\n",
      "Epoch 32: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 991s 276ms/step - loss: 2.0086 - accuracy: 0.3017 - val_loss: 2.7839 - val_accuracy: 0.2540\n",
      "Epoch 33/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9706 - accuracy: 0.3107\n",
      "Epoch 33: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 973s 271ms/step - loss: 1.9706 - accuracy: 0.3107 - val_loss: 3.1785 - val_accuracy: 0.2578\n",
      "Epoch 34/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9185 - accuracy: 0.3218\n",
      "Epoch 34: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 961s 268ms/step - loss: 1.9185 - accuracy: 0.3218 - val_loss: 3.2997 - val_accuracy: 0.2635\n",
      "Epoch 35/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8851 - accuracy: 0.3294\n",
      "Epoch 35: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 977s 272ms/step - loss: 1.8851 - accuracy: 0.3294 - val_loss: 3.8168 - val_accuracy: 0.2554\n",
      "Epoch 36/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8867 - accuracy: 0.3308\n",
      "Epoch 36: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 968s 270ms/step - loss: 1.8867 - accuracy: 0.3308 - val_loss: 3.3489 - val_accuracy: 0.2555\n",
      "Epoch 37/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8370 - accuracy: 0.3403\n",
      "Epoch 37: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 972s 271ms/step - loss: 1.8370 - accuracy: 0.3403 - val_loss: 3.7803 - val_accuracy: 0.2687\n",
      "Epoch 38/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8061 - accuracy: 0.3489\n",
      "Epoch 38: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 998s 278ms/step - loss: 1.8061 - accuracy: 0.3489 - val_loss: 2.9547 - val_accuracy: 0.2742\n",
      "Epoch 39/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7760 - accuracy: 0.3562\n",
      "Epoch 39: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1027s 286ms/step - loss: 1.7760 - accuracy: 0.3562 - val_loss: 3.1922 - val_accuracy: 0.2746\n",
      "Epoch 40/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7638 - accuracy: 0.3600\n",
      "Epoch 40: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 970s 270ms/step - loss: 1.7638 - accuracy: 0.3600 - val_loss: 4.2314 - val_accuracy: 0.2723\n",
      "Epoch 41/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7744 - accuracy: 0.3593\n",
      "Epoch 41: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 968s 270ms/step - loss: 1.7744 - accuracy: 0.3593 - val_loss: 3.2943 - val_accuracy: 0.2744\n",
      "Epoch 42/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7337 - accuracy: 0.3670\n",
      "Epoch 42: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 974s 271ms/step - loss: 1.7337 - accuracy: 0.3670 - val_loss: 4.5665 - val_accuracy: 0.2618\n",
      "Epoch 43/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7029 - accuracy: 0.3744\n",
      "Epoch 43: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 979s 273ms/step - loss: 1.7029 - accuracy: 0.3744 - val_loss: 3.9807 - val_accuracy: 0.2639\n",
      "Epoch 44/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6832 - accuracy: 0.3799\n",
      "Epoch 44: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 935s 261ms/step - loss: 1.6832 - accuracy: 0.3799 - val_loss: 2.8908 - val_accuracy: 0.2966\n",
      "Epoch 45/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6781 - accuracy: 0.3818\n",
      "Epoch 45: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 970s 270ms/step - loss: 1.6781 - accuracy: 0.3818 - val_loss: 3.3511 - val_accuracy: 0.2881\n",
      "Epoch 46/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6706 - accuracy: 0.3846\n",
      "Epoch 46: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 982s 274ms/step - loss: 1.6706 - accuracy: 0.3846 - val_loss: 4.1154 - val_accuracy: 0.2680\n",
      "Epoch 47/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8263 - accuracy: 0.3548\n",
      "Epoch 47: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1041s 290ms/step - loss: 1.8263 - accuracy: 0.3548 - val_loss: 3.7731 - val_accuracy: 0.1877\n",
      "Epoch 48/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1974 - accuracy: 0.2823\n",
      "Epoch 48: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1167s 325ms/step - loss: 2.1974 - accuracy: 0.2823 - val_loss: 3.1661 - val_accuracy: 0.2053\n",
      "Epoch 49/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0761 - accuracy: 0.2884\n",
      "Epoch 49: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1084s 302ms/step - loss: 2.0761 - accuracy: 0.2884 - val_loss: 3.6615 - val_accuracy: 0.2197\n",
      "Epoch 50/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8801 - accuracy: 0.3324\n",
      "Epoch 50: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1041s 290ms/step - loss: 1.8801 - accuracy: 0.3324 - val_loss: 3.1675 - val_accuracy: 0.2627\n",
      "Epoch 51/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7569 - accuracy: 0.3623\n",
      "Epoch 51: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1204s 336ms/step - loss: 1.7569 - accuracy: 0.3623 - val_loss: 3.3998 - val_accuracy: 0.2672\n",
      "Epoch 52/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7007 - accuracy: 0.3759\n",
      "Epoch 52: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1076s 300ms/step - loss: 1.7007 - accuracy: 0.3759 - val_loss: 4.0211 - val_accuracy: 0.2876\n",
      "Epoch 53/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6437 - accuracy: 0.3904\n",
      "Epoch 53: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1056s 294ms/step - loss: 1.6437 - accuracy: 0.3904 - val_loss: 3.8396 - val_accuracy: 0.2783\n",
      "Epoch 54/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6158 - accuracy: 0.3967\n",
      "Epoch 54: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1084s 302ms/step - loss: 1.6158 - accuracy: 0.3967 - val_loss: 4.6001 - val_accuracy: 0.2955\n",
      "Epoch 55/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6004 - accuracy: 0.4014\n",
      "Epoch 55: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1088s 303ms/step - loss: 1.6004 - accuracy: 0.4014 - val_loss: 3.8026 - val_accuracy: 0.2963\n",
      "Epoch 56/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5809 - accuracy: 0.4058\n",
      "Epoch 56: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1093s 305ms/step - loss: 1.5809 - accuracy: 0.4058 - val_loss: 4.4549 - val_accuracy: 0.2879\n",
      "Epoch 57/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5648 - accuracy: 0.4096\n",
      "Epoch 57: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1101s 307ms/step - loss: 1.5648 - accuracy: 0.4096 - val_loss: 3.0656 - val_accuracy: 0.2990\n",
      "Epoch 58/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5590 - accuracy: 0.4113\n",
      "Epoch 58: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1202s 335ms/step - loss: 1.5590 - accuracy: 0.4113 - val_loss: 4.0866 - val_accuracy: 0.2893\n",
      "Epoch 59/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5522 - accuracy: 0.4138\n",
      "Epoch 59: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1286s 358ms/step - loss: 1.5522 - accuracy: 0.4138 - val_loss: 3.9438 - val_accuracy: 0.2958\n",
      "Epoch 60/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5348 - accuracy: 0.4174\n",
      "Epoch 60: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1138s 317ms/step - loss: 1.5348 - accuracy: 0.4174 - val_loss: 2.8902 - val_accuracy: 0.3276\n",
      "Epoch 61/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5304 - accuracy: 0.4186\n",
      "Epoch 61: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1139s 318ms/step - loss: 1.5304 - accuracy: 0.4186 - val_loss: 3.4804 - val_accuracy: 0.3109\n",
      "Epoch 62/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5164 - accuracy: 0.4216\n",
      "Epoch 62: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1147s 320ms/step - loss: 1.5164 - accuracy: 0.4216 - val_loss: 2.3408 - val_accuracy: 0.3316\n",
      "Epoch 63/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5701 - accuracy: 0.4148\n",
      "Epoch 63: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1135s 316ms/step - loss: 1.5701 - accuracy: 0.4148 - val_loss: 3.0052 - val_accuracy: 0.1734\n",
      "Epoch 64/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8648 - accuracy: 0.3520\n",
      "Epoch 64: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1151s 321ms/step - loss: 1.8648 - accuracy: 0.3520 - val_loss: 3.1362 - val_accuracy: 0.2942\n",
      "Epoch 65/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6186 - accuracy: 0.4007\n",
      "Epoch 65: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1129s 315ms/step - loss: 1.6186 - accuracy: 0.4007 - val_loss: 3.6005 - val_accuracy: 0.3094\n",
      "Epoch 66/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5633 - accuracy: 0.4128\n",
      "Epoch 66: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1154s 322ms/step - loss: 1.5633 - accuracy: 0.4128 - val_loss: 3.4714 - val_accuracy: 0.3222\n",
      "Epoch 67/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5618 - accuracy: 0.4130\n",
      "Epoch 67: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1128s 314ms/step - loss: 1.5618 - accuracy: 0.4130 - val_loss: 3.6699 - val_accuracy: 0.3054\n",
      "Epoch 68/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5241 - accuracy: 0.4206\n",
      "Epoch 68: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1123s 313ms/step - loss: 1.5241 - accuracy: 0.4206 - val_loss: 7.1206 - val_accuracy: 0.3183\n",
      "Epoch 69/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5429 - accuracy: 0.4178\n",
      "Epoch 69: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1133s 316ms/step - loss: 1.5429 - accuracy: 0.4178 - val_loss: 4.2722 - val_accuracy: 0.2936\n",
      "Epoch 70/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5025 - accuracy: 0.4268\n",
      "Epoch 70: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1163s 324ms/step - loss: 1.5025 - accuracy: 0.4268 - val_loss: 3.6164 - val_accuracy: 0.3169\n",
      "Epoch 71/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.4902 - accuracy: 0.4291\n",
      "Epoch 71: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1145s 319ms/step - loss: 1.4902 - accuracy: 0.4291 - val_loss: 3.4122 - val_accuracy: 0.3328\n",
      "Epoch 72/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5253 - accuracy: 0.4198\n",
      "Epoch 72: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1102s 307ms/step - loss: 1.5253 - accuracy: 0.4198 - val_loss: 2.5880 - val_accuracy: 0.3265\n",
      "Epoch 73/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5624 - accuracy: 0.4104\n",
      "Epoch 73: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1085s 302ms/step - loss: 1.5624 - accuracy: 0.4104 - val_loss: 2.2976 - val_accuracy: 0.3412\n",
      "Epoch 74/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4528 - accuracy: 0.2750\n",
      "Epoch 74: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1121s 312ms/step - loss: 2.4528 - accuracy: 0.2750 - val_loss: 4.6741 - val_accuracy: 0.1537\n",
      "Epoch 75/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1821 - accuracy: 0.2812\n",
      "Epoch 75: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1139s 317ms/step - loss: 2.1821 - accuracy: 0.2812 - val_loss: 3.2742 - val_accuracy: 0.2361\n",
      "Epoch 76/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8439 - accuracy: 0.3466\n",
      "Epoch 76: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1129s 315ms/step - loss: 1.8439 - accuracy: 0.3466 - val_loss: 2.7634 - val_accuracy: 0.2727\n",
      "Epoch 77/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8593 - accuracy: 0.3463\n",
      "Epoch 77: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1118s 311ms/step - loss: 1.8593 - accuracy: 0.3463 - val_loss: 5.2407 - val_accuracy: 0.2310\n",
      "Epoch 78/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6761 - accuracy: 0.3849\n",
      "Epoch 78: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1126s 314ms/step - loss: 1.6761 - accuracy: 0.3849 - val_loss: 2.5281 - val_accuracy: 0.3161\n",
      "Epoch 79/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5768 - accuracy: 0.4090\n",
      "Epoch 79: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1132s 316ms/step - loss: 1.5768 - accuracy: 0.4090 - val_loss: 3.7682 - val_accuracy: 0.3067\n",
      "Epoch 80/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8285 - accuracy: 0.3626\n",
      "Epoch 80: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1128s 314ms/step - loss: 1.8285 - accuracy: 0.3626 - val_loss: 2.4844 - val_accuracy: 0.2560\n",
      "Epoch 81/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8342 - accuracy: 0.3470\n",
      "Epoch 81: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1125s 314ms/step - loss: 1.8342 - accuracy: 0.3470 - val_loss: 2.5824 - val_accuracy: 0.3065\n",
      "Epoch 82/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6640 - accuracy: 0.3886\n",
      "Epoch 82: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1128s 314ms/step - loss: 1.6640 - accuracy: 0.3886 - val_loss: 2.2165 - val_accuracy: 0.3298\n",
      "Epoch 83/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6479 - accuracy: 0.3960\n",
      "Epoch 83: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1123s 313ms/step - loss: 1.6479 - accuracy: 0.3960 - val_loss: 2.1437 - val_accuracy: 0.3412\n",
      "Epoch 84/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5502 - accuracy: 0.4162\n",
      "Epoch 84: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1121s 312ms/step - loss: 1.5502 - accuracy: 0.4162 - val_loss: 2.1906 - val_accuracy: 0.3437\n",
      "Epoch 85/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1167 - accuracy: 0.3285\n",
      "Epoch 85: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1116s 311ms/step - loss: 2.1167 - accuracy: 0.3285 - val_loss: 2.7826 - val_accuracy: 0.2487\n",
      "Epoch 86/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1224 - accuracy: 0.2957\n",
      "Epoch 86: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1139s 317ms/step - loss: 2.1224 - accuracy: 0.2957 - val_loss: 2.4206 - val_accuracy: 0.2776\n",
      "Epoch 87/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8689 - accuracy: 0.3418\n",
      "Epoch 87: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1173s 327ms/step - loss: 1.8689 - accuracy: 0.3418 - val_loss: 2.0506 - val_accuracy: 0.3274\n",
      "Epoch 88/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7349 - accuracy: 0.3701\n",
      "Epoch 88: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1136s 317ms/step - loss: 1.7349 - accuracy: 0.3701 - val_loss: 2.1562 - val_accuracy: 0.3290\n",
      "Epoch 89/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6732 - accuracy: 0.3847\n",
      "Epoch 89: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1121s 313ms/step - loss: 1.6732 - accuracy: 0.3847 - val_loss: 2.1121 - val_accuracy: 0.3362\n",
      "Epoch 90/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5964 - accuracy: 0.4008\n",
      "Epoch 90: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1136s 317ms/step - loss: 1.5964 - accuracy: 0.4008 - val_loss: 2.4328 - val_accuracy: 0.3236\n",
      "Epoch 91/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.5984 - accuracy: 0.4017\n",
      "Epoch 91: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1135s 316ms/step - loss: 1.5984 - accuracy: 0.4017 - val_loss: 1.8867 - val_accuracy: 0.3515\n",
      "Epoch 92/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6914 - accuracy: 0.3813\n",
      "Epoch 92: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1151s 321ms/step - loss: 1.6914 - accuracy: 0.3813 - val_loss: 1.9164 - val_accuracy: 0.3402\n",
      "Epoch 93/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3130 - accuracy: 0.2769\n",
      "Epoch 93: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1157s 323ms/step - loss: 2.3130 - accuracy: 0.2769 - val_loss: 2.8445 - val_accuracy: 0.2210\n",
      "Epoch 94/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9896 - accuracy: 0.3248\n",
      "Epoch 94: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1134s 316ms/step - loss: 1.9896 - accuracy: 0.3248 - val_loss: 2.2077 - val_accuracy: 0.3092\n",
      "Epoch 95/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.6227 - accuracy: 0.3974\n",
      "Epoch 95: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1152s 321ms/step - loss: 1.6227 - accuracy: 0.3974 - val_loss: 1.8698 - val_accuracy: 0.3475\n",
      "Epoch 96/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3240 - accuracy: 0.2804\n",
      "Epoch 96: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1147s 320ms/step - loss: 2.3240 - accuracy: 0.2804 - val_loss: 2.5714 - val_accuracy: 0.2338\n",
      "Epoch 97/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8394 - accuracy: 0.1907\n",
      "Epoch 97: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1235s 344ms/step - loss: 2.8394 - accuracy: 0.1907 - val_loss: 3.0097 - val_accuracy: 0.1847\n",
      "Epoch 98/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 6.3985 - accuracy: 0.1086\n",
      "Epoch 98: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1267s 353ms/step - loss: 6.3985 - accuracy: 0.1086 - val_loss: 4.3612 - val_accuracy: 0.1011\n",
      "Epoch 99/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1798 - accuracy: 0.1527\n",
      "Epoch 99: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1139s 317ms/step - loss: 3.1798 - accuracy: 0.1527 - val_loss: 4.6379 - val_accuracy: 0.1226\n",
      "Epoch 100/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.7847 - accuracy: 0.1887\n",
      "Epoch 100: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1116s 311ms/step - loss: 2.7847 - accuracy: 0.1887 - val_loss: 5.2567 - val_accuracy: 0.1318\n",
      "Epoch 101/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 6.3761 - accuracy: 0.1830\n",
      "Epoch 101: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1113s 310ms/step - loss: 6.3761 - accuracy: 0.1830 - val_loss: 8.1209 - val_accuracy: 0.0759\n",
      "Epoch 102/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 5.5672 - accuracy: 0.0798\n",
      "Epoch 102: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1106s 308ms/step - loss: 5.5672 - accuracy: 0.0798 - val_loss: 4.7874 - val_accuracy: 0.0981\n",
      "Epoch 103/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.6107 - accuracy: 0.1132\n",
      "Epoch 103: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1572s 438ms/step - loss: 3.6107 - accuracy: 0.1132 - val_loss: 3.3814 - val_accuracy: 0.1272\n",
      "Epoch 104/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1947 - accuracy: 0.1418\n",
      "Epoch 104: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1229s 343ms/step - loss: 3.1947 - accuracy: 0.1418 - val_loss: 3.0829 - val_accuracy: 0.1578\n",
      "Epoch 105/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.0379 - accuracy: 0.1614\n",
      "Epoch 105: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1262s 352ms/step - loss: 3.0379 - accuracy: 0.1614 - val_loss: 3.6973 - val_accuracy: 0.1471\n",
      "Epoch 106/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5787 - accuracy: 0.2153\n",
      "Epoch 106: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1271s 354ms/step - loss: 2.5787 - accuracy: 0.2153 - val_loss: 4.4436 - val_accuracy: 0.1582\n",
      "Epoch 107/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8266 - accuracy: 0.1911\n",
      "Epoch 107: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1235s 344ms/step - loss: 2.8266 - accuracy: 0.1911 - val_loss: 3.3018 - val_accuracy: 0.1579\n",
      "Epoch 108/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4669 - accuracy: 0.2255\n",
      "Epoch 108: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1258s 351ms/step - loss: 2.4669 - accuracy: 0.2255 - val_loss: 4.2338 - val_accuracy: 0.1729\n",
      "Epoch 109/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3959 - accuracy: 0.2370\n",
      "Epoch 109: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1285s 358ms/step - loss: 2.3959 - accuracy: 0.2370 - val_loss: 8.5705 - val_accuracy: 0.1524\n",
      "Epoch 110/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1774 - accuracy: 0.2664\n",
      "Epoch 110: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1346s 375ms/step - loss: 2.1774 - accuracy: 0.2664 - val_loss: 4.7748 - val_accuracy: 0.1821\n",
      "Epoch 111/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0891 - accuracy: 0.2834\n",
      "Epoch 111: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1284s 358ms/step - loss: 2.0891 - accuracy: 0.2834 - val_loss: 4.9517 - val_accuracy: 0.2047\n",
      "Epoch 112/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0061 - accuracy: 0.3043\n",
      "Epoch 112: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1372s 382ms/step - loss: 2.0061 - accuracy: 0.3043 - val_loss: 5.7317 - val_accuracy: 0.1987\n",
      "Epoch 113/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9897 - accuracy: 0.3098\n",
      "Epoch 113: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1323s 369ms/step - loss: 1.9897 - accuracy: 0.3098 - val_loss: 4.1548 - val_accuracy: 0.2043\n",
      "Epoch 114/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1702 - accuracy: 0.2791\n",
      "Epoch 114: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1270s 354ms/step - loss: 2.1702 - accuracy: 0.2791 - val_loss: 4.2186 - val_accuracy: 0.1832\n",
      "Epoch 115/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9367 - accuracy: 0.3178\n",
      "Epoch 115: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1272s 354ms/step - loss: 1.9367 - accuracy: 0.3178 - val_loss: 4.7980 - val_accuracy: 0.1931\n",
      "Epoch 116/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8708 - accuracy: 0.3350\n",
      "Epoch 116: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1260s 351ms/step - loss: 1.8708 - accuracy: 0.3350 - val_loss: 5.6097 - val_accuracy: 0.1851\n",
      "Epoch 117/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8570 - accuracy: 0.3387\n",
      "Epoch 117: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1242s 346ms/step - loss: 1.8570 - accuracy: 0.3387 - val_loss: 6.2016 - val_accuracy: 0.1915\n",
      "Epoch 118/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8030 - accuracy: 0.3507\n",
      "Epoch 118: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1243s 346ms/step - loss: 1.8030 - accuracy: 0.3507 - val_loss: 5.5731 - val_accuracy: 0.2103\n",
      "Epoch 119/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7843 - accuracy: 0.3553\n",
      "Epoch 119: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1304s 364ms/step - loss: 1.7843 - accuracy: 0.3553 - val_loss: 6.1878 - val_accuracy: 0.2040\n",
      "Epoch 120/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7484 - accuracy: 0.3640\n",
      "Epoch 120: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1268s 353ms/step - loss: 1.7484 - accuracy: 0.3640 - val_loss: 3.8905 - val_accuracy: 0.2197\n",
      "Epoch 121/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.7516 - accuracy: 0.3645\n",
      "Epoch 121: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1239s 345ms/step - loss: 1.7516 - accuracy: 0.3645 - val_loss: 6.3737 - val_accuracy: 0.2131\n",
      "Epoch 122/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5597 - accuracy: 0.2405\n",
      "Epoch 122: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1313s 366ms/step - loss: 2.5597 - accuracy: 0.2405 - val_loss: 3.4719 - val_accuracy: 0.1304\n",
      "Epoch 123/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1332 - accuracy: 0.1507\n",
      "Epoch 123: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1247s 347ms/step - loss: 3.1332 - accuracy: 0.1507 - val_loss: 3.7426 - val_accuracy: 0.1001\n",
      "Epoch 124/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1888 - accuracy: 0.1410\n",
      "Epoch 124: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1239s 345ms/step - loss: 3.1888 - accuracy: 0.1410 - val_loss: 4.9112 - val_accuracy: 0.1029\n",
      "Epoch 125/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.0227 - accuracy: 0.1599\n",
      "Epoch 125: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1249s 348ms/step - loss: 3.0227 - accuracy: 0.1599 - val_loss: 5.6345 - val_accuracy: 0.1137\n",
      "Epoch 126/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9313 - accuracy: 0.1720\n",
      "Epoch 126: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1221s 340ms/step - loss: 2.9313 - accuracy: 0.1720 - val_loss: 5.5142 - val_accuracy: 0.1182\n",
      "Epoch 127/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.0225 - accuracy: 0.1585\n",
      "Epoch 127: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1247s 348ms/step - loss: 3.0225 - accuracy: 0.1585 - val_loss: 4.4452 - val_accuracy: 0.1091\n",
      "Epoch 128/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9233 - accuracy: 0.1679\n",
      "Epoch 128: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1236s 345ms/step - loss: 2.9233 - accuracy: 0.1679 - val_loss: 4.2573 - val_accuracy: 0.1164\n",
      "Epoch 129/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8594 - accuracy: 0.1754\n",
      "Epoch 129: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1334s 372ms/step - loss: 2.8594 - accuracy: 0.1754 - val_loss: 3.7179 - val_accuracy: 0.1169\n",
      "Epoch 130/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9252 - accuracy: 0.1690\n",
      "Epoch 130: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1339s 373ms/step - loss: 2.9252 - accuracy: 0.1690 - val_loss: 3.5272 - val_accuracy: 0.1339\n",
      "Epoch 131/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.0879 - accuracy: 0.1546\n",
      "Epoch 131: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1255s 350ms/step - loss: 3.0879 - accuracy: 0.1546 - val_loss: 4.9154 - val_accuracy: 0.1099\n",
      "Epoch 132/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.0547 - accuracy: 0.1571\n",
      "Epoch 132: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1229s 343ms/step - loss: 3.0547 - accuracy: 0.1571 - val_loss: 5.7462 - val_accuracy: 0.0810\n",
      "Epoch 133/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3396 - accuracy: 0.1329\n",
      "Epoch 133: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1245s 347ms/step - loss: 3.3396 - accuracy: 0.1329 - val_loss: 4.9876 - val_accuracy: 0.0826\n",
      "Epoch 134/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.2369 - accuracy: 0.1410\n",
      "Epoch 134: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1241s 346ms/step - loss: 3.2369 - accuracy: 0.1410 - val_loss: 9.7361 - val_accuracy: 0.0741\n",
      "Epoch 135/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.2062 - accuracy: 0.1416\n",
      "Epoch 135: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1249s 348ms/step - loss: 3.2062 - accuracy: 0.1416 - val_loss: 5.0690 - val_accuracy: 0.0967\n",
      "Epoch 136/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9771 - accuracy: 0.1628\n",
      "Epoch 136: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1255s 350ms/step - loss: 2.9771 - accuracy: 0.1628 - val_loss: 5.5432 - val_accuracy: 0.0980\n",
      "Epoch 137/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9433 - accuracy: 0.1599\n",
      "Epoch 137: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1281s 357ms/step - loss: 2.9433 - accuracy: 0.1599 - val_loss: 4.6511 - val_accuracy: 0.1180\n",
      "Epoch 138/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9588 - accuracy: 0.1627\n",
      "Epoch 138: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1226s 342ms/step - loss: 2.9588 - accuracy: 0.1627 - val_loss: 13.1597 - val_accuracy: 0.0754\n",
      "Epoch 139/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9860 - accuracy: 0.1658\n",
      "Epoch 139: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1244s 347ms/step - loss: 2.9860 - accuracy: 0.1658 - val_loss: 6.9186 - val_accuracy: 0.0884\n",
      "Epoch 140/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1796 - accuracy: 0.1501\n",
      "Epoch 140: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1238s 345ms/step - loss: 3.1796 - accuracy: 0.1501 - val_loss: 6.6377 - val_accuracy: 0.0726\n",
      "Epoch 141/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3698 - accuracy: 0.1321\n",
      "Epoch 141: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1283s 357ms/step - loss: 3.3698 - accuracy: 0.1321 - val_loss: 16.7945 - val_accuracy: 0.0667\n",
      "Epoch 142/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3405 - accuracy: 0.1345\n",
      "Epoch 142: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1272s 354ms/step - loss: 3.3405 - accuracy: 0.1345 - val_loss: 9.1282 - val_accuracy: 0.0890\n",
      "Epoch 143/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1457 - accuracy: 0.1522\n",
      "Epoch 143: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1235s 344ms/step - loss: 3.1457 - accuracy: 0.1522 - val_loss: 10.9252 - val_accuracy: 0.0876\n",
      "Epoch 144/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8685 - accuracy: 0.1783\n",
      "Epoch 144: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1255s 350ms/step - loss: 2.8685 - accuracy: 0.1783 - val_loss: 11.8729 - val_accuracy: 0.0950\n",
      "Epoch 145/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6678 - accuracy: 0.1998\n",
      "Epoch 145: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1273s 355ms/step - loss: 2.6678 - accuracy: 0.1998 - val_loss: 11.0232 - val_accuracy: 0.1189\n",
      "Epoch 146/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6119 - accuracy: 0.2094\n",
      "Epoch 146: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1241s 346ms/step - loss: 2.6119 - accuracy: 0.2094 - val_loss: 10.6844 - val_accuracy: 0.1146\n",
      "Epoch 147/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4376 - accuracy: 0.2345\n",
      "Epoch 147: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1249s 348ms/step - loss: 2.4376 - accuracy: 0.2345 - val_loss: 9.7757 - val_accuracy: 0.1579\n",
      "Epoch 148/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4008 - accuracy: 0.2409\n",
      "Epoch 148: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1257s 350ms/step - loss: 2.4008 - accuracy: 0.2409 - val_loss: 9.0501 - val_accuracy: 0.1353\n",
      "Epoch 149/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6019 - accuracy: 0.2142\n",
      "Epoch 149: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1244s 347ms/step - loss: 2.6019 - accuracy: 0.2142 - val_loss: 8.7090 - val_accuracy: 0.1234\n",
      "Epoch 150/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3803 - accuracy: 0.2484\n",
      "Epoch 150: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1157s 323ms/step - loss: 2.3803 - accuracy: 0.2484 - val_loss: 8.4368 - val_accuracy: 0.1698\n",
      "Epoch 151/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2156 - accuracy: 0.2732\n",
      "Epoch 151: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 999s 278ms/step - loss: 2.2156 - accuracy: 0.2732 - val_loss: 8.9470 - val_accuracy: 0.1530\n",
      "Epoch 152/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1275 - accuracy: 0.2878\n",
      "Epoch 152: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1122s 313ms/step - loss: 2.1275 - accuracy: 0.2878 - val_loss: 14.4382 - val_accuracy: 0.1542\n",
      "Epoch 153/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0698 - accuracy: 0.2992\n",
      "Epoch 153: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1039s 289ms/step - loss: 2.0698 - accuracy: 0.2992 - val_loss: 11.1619 - val_accuracy: 0.1659\n",
      "Epoch 154/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0384 - accuracy: 0.3054\n",
      "Epoch 154: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1005s 280ms/step - loss: 2.0384 - accuracy: 0.3054 - val_loss: 22.5082 - val_accuracy: 0.1422\n",
      "Epoch 155/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9991 - accuracy: 0.3129\n",
      "Epoch 155: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 989s 276ms/step - loss: 1.9991 - accuracy: 0.3129 - val_loss: 14.9859 - val_accuracy: 0.1580\n",
      "Epoch 156/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9588 - accuracy: 0.3205\n",
      "Epoch 156: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 996s 278ms/step - loss: 1.9588 - accuracy: 0.3205 - val_loss: 19.4333 - val_accuracy: 0.1510\n",
      "Epoch 157/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9312 - accuracy: 0.3258\n",
      "Epoch 157: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1031s 287ms/step - loss: 1.9312 - accuracy: 0.3258 - val_loss: 17.6299 - val_accuracy: 0.1697\n",
      "Epoch 158/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9305 - accuracy: 0.3268\n",
      "Epoch 158: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1062s 296ms/step - loss: 1.9305 - accuracy: 0.3268 - val_loss: 20.5305 - val_accuracy: 0.1646\n",
      "Epoch 159/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8956 - accuracy: 0.3328\n",
      "Epoch 159: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1133s 316ms/step - loss: 1.8956 - accuracy: 0.3328 - val_loss: 16.6778 - val_accuracy: 0.1580\n",
      "Epoch 160/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8832 - accuracy: 0.3359\n",
      "Epoch 160: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1050s 293ms/step - loss: 1.8832 - accuracy: 0.3359 - val_loss: 17.2953 - val_accuracy: 0.1724\n",
      "Epoch 161/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8504 - accuracy: 0.3422\n",
      "Epoch 161: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1048s 292ms/step - loss: 1.8504 - accuracy: 0.3422 - val_loss: 10.2176 - val_accuracy: 0.2046\n",
      "Epoch 162/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.8968 - accuracy: 0.3366\n",
      "Epoch 162: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1006s 280ms/step - loss: 1.8968 - accuracy: 0.3366 - val_loss: 16.1832 - val_accuracy: 0.1886\n",
      "Epoch 163/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3239 - accuracy: 0.2632\n",
      "Epoch 163: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1042s 290ms/step - loss: 2.3239 - accuracy: 0.2632 - val_loss: 2.4628 - val_accuracy: 0.2386\n",
      "Epoch 164/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 11.2700 - accuracy: 0.0859\n",
      "Epoch 164: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1045s 291ms/step - loss: 11.2700 - accuracy: 0.0859 - val_loss: 5.9638 - val_accuracy: 0.0493\n",
      "Epoch 165/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.5199 - accuracy: 0.0787\n",
      "Epoch 165: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1051s 293ms/step - loss: 4.5199 - accuracy: 0.0787 - val_loss: 3.9310 - val_accuracy: 0.0983\n",
      "Epoch 166/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.0400 - accuracy: 0.1579\n",
      "Epoch 166: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1002s 279ms/step - loss: 3.0400 - accuracy: 0.1579 - val_loss: 3.2409 - val_accuracy: 0.1678\n",
      "Epoch 167/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3916 - accuracy: 0.2373\n",
      "Epoch 167: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1022s 285ms/step - loss: 2.3916 - accuracy: 0.2373 - val_loss: 3.6948 - val_accuracy: 0.2118\n",
      "Epoch 168/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8453 - accuracy: 0.1939\n",
      "Epoch 168: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1118s 311ms/step - loss: 2.8453 - accuracy: 0.1939 - val_loss: 3.8039 - val_accuracy: 0.1652\n",
      "Epoch 169/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3481 - accuracy: 0.2420\n",
      "Epoch 169: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1079s 301ms/step - loss: 2.3481 - accuracy: 0.2420 - val_loss: 3.2496 - val_accuracy: 0.2005\n",
      "Epoch 170/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8441 - accuracy: 0.1955\n",
      "Epoch 170: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1134s 316ms/step - loss: 2.8441 - accuracy: 0.1955 - val_loss: 3.5066 - val_accuracy: 0.1416\n",
      "Epoch 171/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8550 - accuracy: 0.1810\n",
      "Epoch 171: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1086s 303ms/step - loss: 2.8550 - accuracy: 0.1810 - val_loss: 4.0581 - val_accuracy: 0.1485\n",
      "Epoch 172/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4883 - accuracy: 0.2255\n",
      "Epoch 172: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1208s 337ms/step - loss: 2.4883 - accuracy: 0.2255 - val_loss: 2.6402 - val_accuracy: 0.2098\n",
      "Epoch 173/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.2412 - accuracy: 0.1133\n",
      "Epoch 173: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1308s 365ms/step - loss: 4.2412 - accuracy: 0.1133 - val_loss: 4.4921 - val_accuracy: 0.1092\n",
      "Epoch 174/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.2874 - accuracy: 0.1395\n",
      "Epoch 174: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1151s 321ms/step - loss: 3.2874 - accuracy: 0.1395 - val_loss: 3.2679 - val_accuracy: 0.1344\n",
      "Epoch 175/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8897 - accuracy: 0.1729\n",
      "Epoch 175: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1123s 313ms/step - loss: 2.8897 - accuracy: 0.1729 - val_loss: 3.1627 - val_accuracy: 0.1567\n",
      "Epoch 176/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6316 - accuracy: 0.2024\n",
      "Epoch 176: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1212s 338ms/step - loss: 2.6316 - accuracy: 0.2024 - val_loss: 3.7944 - val_accuracy: 0.1702\n",
      "Epoch 177/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4520 - accuracy: 0.2250\n",
      "Epoch 177: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1067s 297ms/step - loss: 2.4520 - accuracy: 0.2250 - val_loss: 2.9336 - val_accuracy: 0.2044\n",
      "Epoch 178/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3899 - accuracy: 0.2346\n",
      "Epoch 178: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1183s 330ms/step - loss: 2.3899 - accuracy: 0.2346 - val_loss: 2.6076 - val_accuracy: 0.2080\n",
      "Epoch 179/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5762 - accuracy: 0.2214\n",
      "Epoch 179: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1165s 325ms/step - loss: 2.5762 - accuracy: 0.2214 - val_loss: 4.7451 - val_accuracy: 0.0982\n",
      "Epoch 180/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6530 - accuracy: 0.1999\n",
      "Epoch 180: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1119s 312ms/step - loss: 2.6530 - accuracy: 0.1999 - val_loss: 2.9445 - val_accuracy: 0.2138\n",
      "Epoch 181/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3870 - accuracy: 0.2368\n",
      "Epoch 181: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1081s 301ms/step - loss: 2.3870 - accuracy: 0.2368 - val_loss: 2.6821 - val_accuracy: 0.2126\n",
      "Epoch 182/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2886 - accuracy: 0.2511\n",
      "Epoch 182: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1040s 290ms/step - loss: 2.2886 - accuracy: 0.2511 - val_loss: 3.2103 - val_accuracy: 0.2239\n",
      "Epoch 183/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4848 - accuracy: 0.2311\n",
      "Epoch 183: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1026s 286ms/step - loss: 2.4848 - accuracy: 0.2311 - val_loss: 4.1139 - val_accuracy: 0.1879\n",
      "Epoch 184/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4898 - accuracy: 0.2264\n",
      "Epoch 184: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1046s 292ms/step - loss: 2.4898 - accuracy: 0.2264 - val_loss: 2.5942 - val_accuracy: 0.2229\n",
      "Epoch 185/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6575 - accuracy: 0.2209\n",
      "Epoch 185: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1096s 305ms/step - loss: 2.6575 - accuracy: 0.2209 - val_loss: 3.4887 - val_accuracy: 0.1594\n",
      "Epoch 186/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4955 - accuracy: 0.2255\n",
      "Epoch 186: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1082s 301ms/step - loss: 2.4955 - accuracy: 0.2255 - val_loss: 4.0024 - val_accuracy: 0.2043\n",
      "Epoch 187/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2080 - accuracy: 0.2627\n",
      "Epoch 187: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1109s 309ms/step - loss: 2.2080 - accuracy: 0.2627 - val_loss: 3.5093 - val_accuracy: 0.2125\n",
      "Epoch 188/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1643 - accuracy: 0.2703\n",
      "Epoch 188: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1191s 332ms/step - loss: 2.1643 - accuracy: 0.2703 - val_loss: 3.2360 - val_accuracy: 0.2169\n",
      "Epoch 189/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2043 - accuracy: 0.2666\n",
      "Epoch 189: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1202s 335ms/step - loss: 2.2043 - accuracy: 0.2666 - val_loss: 2.7161 - val_accuracy: 0.2377\n",
      "Epoch 190/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1602 - accuracy: 0.2733\n",
      "Epoch 190: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1133s 316ms/step - loss: 2.1602 - accuracy: 0.2733 - val_loss: 3.1082 - val_accuracy: 0.2218\n",
      "Epoch 191/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2623 - accuracy: 0.2630\n",
      "Epoch 191: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1100s 306ms/step - loss: 2.2623 - accuracy: 0.2630 - val_loss: 3.3208 - val_accuracy: 0.2067\n",
      "Epoch 192/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1191 - accuracy: 0.2797\n",
      "Epoch 192: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1104s 308ms/step - loss: 2.1191 - accuracy: 0.2797 - val_loss: 2.8834 - val_accuracy: 0.2200\n",
      "Epoch 193/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0778 - accuracy: 0.2866\n",
      "Epoch 193: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1081s 301ms/step - loss: 2.0778 - accuracy: 0.2866 - val_loss: 3.5119 - val_accuracy: 0.2336\n",
      "Epoch 194/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0555 - accuracy: 0.2908\n",
      "Epoch 194: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1102s 307ms/step - loss: 2.0555 - accuracy: 0.2908 - val_loss: 3.2692 - val_accuracy: 0.2273\n",
      "Epoch 195/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0394 - accuracy: 0.2949\n",
      "Epoch 195: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1218s 339ms/step - loss: 2.0394 - accuracy: 0.2949 - val_loss: 3.4950 - val_accuracy: 0.2209\n",
      "Epoch 196/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1472 - accuracy: 0.2812\n",
      "Epoch 196: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1133s 316ms/step - loss: 2.1472 - accuracy: 0.2812 - val_loss: 3.8234 - val_accuracy: 0.1865\n",
      "Epoch 197/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1995 - accuracy: 0.2690\n",
      "Epoch 197: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1238s 345ms/step - loss: 2.1995 - accuracy: 0.2690 - val_loss: 5.3971 - val_accuracy: 0.1866\n",
      "Epoch 198/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3423 - accuracy: 0.2505\n",
      "Epoch 198: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1321s 368ms/step - loss: 2.3423 - accuracy: 0.2505 - val_loss: 3.9745 - val_accuracy: 0.1551\n",
      "Epoch 199/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2664 - accuracy: 0.2555\n",
      "Epoch 199: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1360s 379ms/step - loss: 2.2664 - accuracy: 0.2555 - val_loss: 3.7200 - val_accuracy: 0.2159\n",
      "Epoch 200/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0702 - accuracy: 0.2858\n",
      "Epoch 200: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1214s 338ms/step - loss: 2.0702 - accuracy: 0.2858 - val_loss: 3.4782 - val_accuracy: 0.2293\n",
      "Epoch 201/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0540 - accuracy: 0.2904\n",
      "Epoch 201: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1186s 331ms/step - loss: 2.0540 - accuracy: 0.2904 - val_loss: 3.4440 - val_accuracy: 0.2125\n",
      "Epoch 202/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4282 - accuracy: 0.2440\n",
      "Epoch 202: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1213s 338ms/step - loss: 2.4282 - accuracy: 0.2440 - val_loss: 3.4608 - val_accuracy: 0.1787\n",
      "Epoch 203/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4968 - accuracy: 0.2306\n",
      "Epoch 203: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1207s 336ms/step - loss: 2.4968 - accuracy: 0.2306 - val_loss: 2.8528 - val_accuracy: 0.2204\n",
      "Epoch 204/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3269 - accuracy: 0.2504\n",
      "Epoch 204: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1205s 336ms/step - loss: 2.3269 - accuracy: 0.2504 - val_loss: 3.8388 - val_accuracy: 0.1957\n",
      "Epoch 205/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2226 - accuracy: 0.2618\n",
      "Epoch 205: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1218s 339ms/step - loss: 2.2226 - accuracy: 0.2618 - val_loss: 3.1877 - val_accuracy: 0.2302\n",
      "Epoch 206/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2390 - accuracy: 0.2605\n",
      "Epoch 206: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1201s 335ms/step - loss: 2.2390 - accuracy: 0.2605 - val_loss: 5.2645 - val_accuracy: 0.2199\n",
      "Epoch 207/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1518 - accuracy: 0.2714\n",
      "Epoch 207: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1220s 340ms/step - loss: 2.1518 - accuracy: 0.2714 - val_loss: 3.5398 - val_accuracy: 0.2347\n",
      "Epoch 208/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6684 - accuracy: 0.2147\n",
      "Epoch 208: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1187s 331ms/step - loss: 2.6684 - accuracy: 0.2147 - val_loss: 3.9933 - val_accuracy: 0.1554\n",
      "Epoch 209/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4720 - accuracy: 0.2281\n",
      "Epoch 209: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1211s 337ms/step - loss: 2.4720 - accuracy: 0.2281 - val_loss: 3.3132 - val_accuracy: 0.2150\n",
      "Epoch 210/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2011 - accuracy: 0.2650\n",
      "Epoch 210: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1178s 328ms/step - loss: 2.2011 - accuracy: 0.2650 - val_loss: 3.0434 - val_accuracy: 0.2249\n",
      "Epoch 211/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2717 - accuracy: 0.2612\n",
      "Epoch 211: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1213s 338ms/step - loss: 2.2717 - accuracy: 0.2612 - val_loss: 3.2541 - val_accuracy: 0.1513\n",
      "Epoch 212/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9255 - accuracy: 0.1834\n",
      "Epoch 212: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1183s 330ms/step - loss: 2.9255 - accuracy: 0.1834 - val_loss: 3.4608 - val_accuracy: 0.1758\n",
      "Epoch 213/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8844 - accuracy: 0.1776\n",
      "Epoch 213: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1211s 338ms/step - loss: 2.8844 - accuracy: 0.1776 - val_loss: 3.7985 - val_accuracy: 0.1599\n",
      "Epoch 214/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.7805 - accuracy: 0.1876\n",
      "Epoch 214: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1183s 330ms/step - loss: 2.7805 - accuracy: 0.1876 - val_loss: 23.8617 - val_accuracy: 0.1281\n",
      "Epoch 215/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5043 - accuracy: 0.2153\n",
      "Epoch 215: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1203s 335ms/step - loss: 2.5043 - accuracy: 0.2153 - val_loss: 4.2404 - val_accuracy: 0.1686\n",
      "Epoch 216/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4005 - accuracy: 0.2302\n",
      "Epoch 216: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1212s 338ms/step - loss: 2.4005 - accuracy: 0.2302 - val_loss: 3.0311 - val_accuracy: 0.1931\n",
      "Epoch 217/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3070 - accuracy: 0.2449\n",
      "Epoch 217: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1253s 349ms/step - loss: 2.3070 - accuracy: 0.2449 - val_loss: 3.5633 - val_accuracy: 0.2083\n",
      "Epoch 218/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2617 - accuracy: 0.2533\n",
      "Epoch 218: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1279s 357ms/step - loss: 2.2617 - accuracy: 0.2533 - val_loss: 3.4039 - val_accuracy: 0.2141\n",
      "Epoch 219/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2506 - accuracy: 0.2637\n",
      "Epoch 219: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1212s 338ms/step - loss: 2.2506 - accuracy: 0.2637 - val_loss: 3.3295 - val_accuracy: 0.2177\n",
      "Epoch 220/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3806 - accuracy: 0.2524\n",
      "Epoch 220: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1199s 334ms/step - loss: 2.3806 - accuracy: 0.2524 - val_loss: 3.4457 - val_accuracy: 0.1956\n",
      "Epoch 221/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1916 - accuracy: 0.1803\n",
      "Epoch 221: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1264s 352ms/step - loss: 3.1916 - accuracy: 0.1803 - val_loss: 3.4267 - val_accuracy: 0.1248\n",
      "Epoch 222/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6513 - accuracy: 0.2105\n",
      "Epoch 222: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1236s 344ms/step - loss: 2.6513 - accuracy: 0.2105 - val_loss: 3.1427 - val_accuracy: 0.1863\n",
      "Epoch 223/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3904 - accuracy: 0.2393\n",
      "Epoch 223: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1170s 326ms/step - loss: 2.3904 - accuracy: 0.2393 - val_loss: 2.8615 - val_accuracy: 0.2035\n",
      "Epoch 224/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2509 - accuracy: 0.2577\n",
      "Epoch 224: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1221s 340ms/step - loss: 2.2509 - accuracy: 0.2577 - val_loss: 2.8567 - val_accuracy: 0.2261\n",
      "Epoch 225/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1563 - accuracy: 0.2722\n",
      "Epoch 225: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1149s 320ms/step - loss: 2.1563 - accuracy: 0.2722 - val_loss: 2.9923 - val_accuracy: 0.2392\n",
      "Epoch 226/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2679 - accuracy: 0.2595\n",
      "Epoch 226: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1308s 365ms/step - loss: 2.2679 - accuracy: 0.2595 - val_loss: 3.5505 - val_accuracy: 0.2103\n",
      "Epoch 227/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2092 - accuracy: 0.2674\n",
      "Epoch 227: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1269s 354ms/step - loss: 2.2092 - accuracy: 0.2674 - val_loss: 3.6981 - val_accuracy: 0.2114\n",
      "Epoch 228/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4457 - accuracy: 0.2431\n",
      "Epoch 228: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1125s 314ms/step - loss: 2.4457 - accuracy: 0.2431 - val_loss: 3.5018 - val_accuracy: 0.2104\n",
      "Epoch 229/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1475 - accuracy: 0.2781\n",
      "Epoch 229: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1178s 328ms/step - loss: 2.1475 - accuracy: 0.2781 - val_loss: 3.0469 - val_accuracy: 0.2415\n",
      "Epoch 230/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2022 - accuracy: 0.2717\n",
      "Epoch 230: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1228s 342ms/step - loss: 2.2022 - accuracy: 0.2717 - val_loss: 2.5018 - val_accuracy: 0.2459\n",
      "Epoch 231/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1202 - accuracy: 0.2855\n",
      "Epoch 231: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1115s 311ms/step - loss: 2.1202 - accuracy: 0.2855 - val_loss: 2.5024 - val_accuracy: 0.2494\n",
      "Epoch 232/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4107 - accuracy: 0.2507\n",
      "Epoch 232: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1175s 328ms/step - loss: 2.4107 - accuracy: 0.2507 - val_loss: 3.5481 - val_accuracy: 0.1598\n",
      "Epoch 233/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.7201 - accuracy: 0.2019\n",
      "Epoch 233: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1134s 316ms/step - loss: 2.7201 - accuracy: 0.2019 - val_loss: 3.0726 - val_accuracy: 0.1949\n",
      "Epoch 234/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5906 - accuracy: 0.2153\n",
      "Epoch 234: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1143s 319ms/step - loss: 2.5906 - accuracy: 0.2153 - val_loss: 3.4333 - val_accuracy: 0.1505\n",
      "Epoch 235/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5700 - accuracy: 0.2175\n",
      "Epoch 235: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1103s 307ms/step - loss: 2.5700 - accuracy: 0.2175 - val_loss: 3.5949 - val_accuracy: 0.1862\n",
      "Epoch 236/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4845 - accuracy: 0.2267\n",
      "Epoch 236: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1119s 312ms/step - loss: 2.4845 - accuracy: 0.2267 - val_loss: 3.6163 - val_accuracy: 0.1908\n",
      "Epoch 237/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2309 - accuracy: 0.2618\n",
      "Epoch 237: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1138s 317ms/step - loss: 2.2309 - accuracy: 0.2618 - val_loss: 3.3993 - val_accuracy: 0.2043\n",
      "Epoch 238/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1852 - accuracy: 0.2714\n",
      "Epoch 238: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1109s 309ms/step - loss: 2.1852 - accuracy: 0.2714 - val_loss: 3.2763 - val_accuracy: 0.2304\n",
      "Epoch 239/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1559 - accuracy: 0.2787\n",
      "Epoch 239: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1155s 322ms/step - loss: 2.1559 - accuracy: 0.2787 - val_loss: 3.9638 - val_accuracy: 0.1699\n",
      "Epoch 240/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5493 - accuracy: 0.2229\n",
      "Epoch 240: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1146s 319ms/step - loss: 2.5493 - accuracy: 0.2229 - val_loss: 3.3068 - val_accuracy: 0.1961\n",
      "Epoch 241/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2372 - accuracy: 0.2597\n",
      "Epoch 241: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1149s 320ms/step - loss: 2.2372 - accuracy: 0.2597 - val_loss: 4.0383 - val_accuracy: 0.1934\n",
      "Epoch 242/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1643 - accuracy: 0.2718\n",
      "Epoch 242: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1115s 311ms/step - loss: 2.1643 - accuracy: 0.2718 - val_loss: 3.3364 - val_accuracy: 0.2156\n",
      "Epoch 243/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1427 - accuracy: 0.2791\n",
      "Epoch 243: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1122s 313ms/step - loss: 2.1427 - accuracy: 0.2791 - val_loss: 2.8026 - val_accuracy: 0.2283\n",
      "Epoch 244/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2371 - accuracy: 0.2656\n",
      "Epoch 244: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1201s 335ms/step - loss: 2.2371 - accuracy: 0.2656 - val_loss: 3.6710 - val_accuracy: 0.1815\n",
      "Epoch 245/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4939 - accuracy: 0.2309\n",
      "Epoch 245: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1144s 319ms/step - loss: 2.4939 - accuracy: 0.2309 - val_loss: 9.2887 - val_accuracy: 0.1504\n",
      "Epoch 246/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.3012 - accuracy: 0.2546\n",
      "Epoch 246: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1128s 314ms/step - loss: 2.3012 - accuracy: 0.2546 - val_loss: 4.0361 - val_accuracy: 0.1780\n",
      "Epoch 247/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1412 - accuracy: 0.2765\n",
      "Epoch 247: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1126s 314ms/step - loss: 2.1412 - accuracy: 0.2765 - val_loss: 3.1432 - val_accuracy: 0.2130\n",
      "Epoch 248/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.1574 - accuracy: 0.2749\n",
      "Epoch 248: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1098s 306ms/step - loss: 2.1574 - accuracy: 0.2749 - val_loss: 3.0116 - val_accuracy: 0.1678\n",
      "Epoch 249/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.2317 - accuracy: 0.2608\n",
      "Epoch 249: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1160s 323ms/step - loss: 2.2317 - accuracy: 0.2608 - val_loss: 3.0144 - val_accuracy: 0.2460\n",
      "Epoch 250/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0902 - accuracy: 0.2881\n",
      "Epoch 250: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1097s 306ms/step - loss: 2.0902 - accuracy: 0.2881 - val_loss: 3.3499 - val_accuracy: 0.2251\n",
      "Epoch 251/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 1.9998 - accuracy: 0.3034\n",
      "Epoch 251: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1087s 303ms/step - loss: 1.9998 - accuracy: 0.3034 - val_loss: 3.1479 - val_accuracy: 0.2511\n",
      "Epoch 252/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.0854 - accuracy: 0.2936\n",
      "Epoch 252: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1092s 304ms/step - loss: 2.0854 - accuracy: 0.2936 - val_loss: 3.0584 - val_accuracy: 0.2450\n",
      "Epoch 253/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.6025 - accuracy: 0.1156\n",
      "Epoch 253: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1108s 309ms/step - loss: 3.6025 - accuracy: 0.1156 - val_loss: 6.4215 - val_accuracy: 0.0859\n",
      "Epoch 254/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.4784 - accuracy: 0.1192\n",
      "Epoch 254: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1092s 304ms/step - loss: 3.4784 - accuracy: 0.1192 - val_loss: 6.3624 - val_accuracy: 0.0752\n",
      "Epoch 255/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3010 - accuracy: 0.1318\n",
      "Epoch 255: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1110s 309ms/step - loss: 3.3010 - accuracy: 0.1318 - val_loss: 6.3319 - val_accuracy: 0.1130\n",
      "Epoch 256/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.8282 - accuracy: 0.1757\n",
      "Epoch 256: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1129s 315ms/step - loss: 2.8282 - accuracy: 0.1757 - val_loss: 6.1753 - val_accuracy: 0.1376\n",
      "Epoch 257/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5926 - accuracy: 0.2027\n",
      "Epoch 257: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1078s 300ms/step - loss: 2.5926 - accuracy: 0.2027 - val_loss: 4.2425 - val_accuracy: 0.1649\n",
      "Epoch 258/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4297 - accuracy: 0.2233\n",
      "Epoch 258: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1086s 303ms/step - loss: 2.4297 - accuracy: 0.2233 - val_loss: 3.1449 - val_accuracy: 0.1762\n",
      "Epoch 259/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5417 - accuracy: 0.2108\n",
      "Epoch 259: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1121s 312ms/step - loss: 2.5417 - accuracy: 0.2108 - val_loss: 6.0515 - val_accuracy: 0.1781\n",
      "Epoch 260/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5131 - accuracy: 0.2177\n",
      "Epoch 260: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1074s 299ms/step - loss: 2.5131 - accuracy: 0.2177 - val_loss: 3.6795 - val_accuracy: 0.1879\n",
      "Epoch 261/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4197 - accuracy: 0.2321\n",
      "Epoch 261: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1132s 316ms/step - loss: 2.4197 - accuracy: 0.2321 - val_loss: 8.7098 - val_accuracy: 0.1939\n",
      "Epoch 262/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5600 - accuracy: 0.2099\n",
      "Epoch 262: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1077s 300ms/step - loss: 2.5600 - accuracy: 0.2099 - val_loss: 2.7557 - val_accuracy: 0.1974\n",
      "Epoch 263/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.4355 - accuracy: 0.2268\n",
      "Epoch 263: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1135s 316ms/step - loss: 2.4355 - accuracy: 0.2268 - val_loss: 2.5548 - val_accuracy: 0.2244\n",
      "Epoch 264/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.6616 - accuracy: 0.2027\n",
      "Epoch 264: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1059s 295ms/step - loss: 2.6616 - accuracy: 0.2027 - val_loss: 3.7341 - val_accuracy: 0.1744\n",
      "Epoch 265/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.9008 - accuracy: 0.1739\n",
      "Epoch 265: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1032s 288ms/step - loss: 2.9008 - accuracy: 0.1739 - val_loss: 3.3330 - val_accuracy: 0.1499\n",
      "Epoch 266/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.7871 - accuracy: 0.1885\n",
      "Epoch 266: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1014s 283ms/step - loss: 2.7871 - accuracy: 0.1885 - val_loss: 3.4146 - val_accuracy: 0.1575\n",
      "Epoch 267/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5484 - accuracy: 0.2164\n",
      "Epoch 267: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 992s 276ms/step - loss: 2.5484 - accuracy: 0.2164 - val_loss: 3.5954 - val_accuracy: 0.1688\n",
      "Epoch 268/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 2.5302 - accuracy: 0.2228\n",
      "Epoch 268: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 987s 275ms/step - loss: 2.5302 - accuracy: 0.2228 - val_loss: 3.3518 - val_accuracy: 0.1851\n",
      "Epoch 269/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 18.7793 - accuracy: 0.0959\n",
      "Epoch 269: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1002s 279ms/step - loss: 18.7793 - accuracy: 0.0959 - val_loss: 5.5673 - val_accuracy: 0.0825\n",
      "Epoch 270/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.3936 - accuracy: 0.0951\n",
      "Epoch 270: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 983s 274ms/step - loss: 4.3936 - accuracy: 0.0951 - val_loss: 27.7083 - val_accuracy: 0.0799\n",
      "Epoch 271/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.5831 - accuracy: 0.1123\n",
      "Epoch 271: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1049s 292ms/step - loss: 3.5831 - accuracy: 0.1123 - val_loss: 5.3029 - val_accuracy: 0.0999\n",
      "Epoch 272/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.4257 - accuracy: 0.1176\n",
      "Epoch 272: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1144s 319ms/step - loss: 3.4257 - accuracy: 0.1176 - val_loss: 4.0546 - val_accuracy: 0.0878\n",
      "Epoch 273/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.3142 - accuracy: 0.1226\n",
      "Epoch 273: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1116s 311ms/step - loss: 3.3142 - accuracy: 0.1226 - val_loss: 5.3241 - val_accuracy: 0.0856\n",
      "Epoch 274/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1793 - accuracy: 0.1378\n",
      "Epoch 274: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1124s 313ms/step - loss: 3.1793 - accuracy: 0.1378 - val_loss: 5.3935 - val_accuracy: 0.0890\n",
      "Epoch 275/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.4305 - accuracy: 0.1238\n",
      "Epoch 275: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1129s 315ms/step - loss: 3.4305 - accuracy: 0.1238 - val_loss: 5.6838 - val_accuracy: 0.0427\n",
      "Epoch 276/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.8297 - accuracy: 0.0934\n",
      "Epoch 276: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1134s 316ms/step - loss: 3.8297 - accuracy: 0.0934 - val_loss: 4.5332 - val_accuracy: 0.0701\n",
      "Epoch 277/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.6622 - accuracy: 0.1096\n",
      "Epoch 277: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1127s 314ms/step - loss: 3.6622 - accuracy: 0.1096 - val_loss: 4.5453 - val_accuracy: 0.0742\n",
      "Epoch 278/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.6696 - accuracy: 0.1087\n",
      "Epoch 278: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1155s 322ms/step - loss: 3.6696 - accuracy: 0.1087 - val_loss: 4.2964 - val_accuracy: 0.1039\n",
      "Epoch 279/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.7265 - accuracy: 0.1077\n",
      "Epoch 279: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1138s 317ms/step - loss: 3.7265 - accuracy: 0.1077 - val_loss: 4.2057 - val_accuracy: 0.0719\n",
      "Epoch 280/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.7902 - accuracy: 0.1036\n",
      "Epoch 280: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1156s 322ms/step - loss: 3.7902 - accuracy: 0.1036 - val_loss: 3.9658 - val_accuracy: 0.0837\n",
      "Epoch 281/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.0228 - accuracy: 0.0830\n",
      "Epoch 281: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1149s 320ms/step - loss: 4.0228 - accuracy: 0.0830 - val_loss: 4.7001 - val_accuracy: 0.0694\n",
      "Epoch 282/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.0556 - accuracy: 0.0816\n",
      "Epoch 282: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1241s 346ms/step - loss: 4.0556 - accuracy: 0.0816 - val_loss: 4.1665 - val_accuracy: 0.0832\n",
      "Epoch 283/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.8750 - accuracy: 0.0946\n",
      "Epoch 283: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1177s 328ms/step - loss: 3.8750 - accuracy: 0.0946 - val_loss: 3.7662 - val_accuracy: 0.0937\n",
      "Epoch 284/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.5606 - accuracy: 0.1158\n",
      "Epoch 284: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1177s 328ms/step - loss: 3.5606 - accuracy: 0.1158 - val_loss: 3.7650 - val_accuracy: 0.0979\n",
      "Epoch 285/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.4197 - accuracy: 0.1282\n",
      "Epoch 285: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1186s 331ms/step - loss: 3.4197 - accuracy: 0.1282 - val_loss: 3.7240 - val_accuracy: 0.1047\n",
      "Epoch 286/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1444 - accuracy: 0.1507\n",
      "Epoch 286: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1162s 324ms/step - loss: 3.1444 - accuracy: 0.1507 - val_loss: 4.2476 - val_accuracy: 0.1137\n",
      "Epoch 287/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.1855 - accuracy: 0.1456\n",
      "Epoch 287: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1197s 334ms/step - loss: 3.1855 - accuracy: 0.1456 - val_loss: 3.9139 - val_accuracy: 0.0938\n",
      "Epoch 288/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.5588 - accuracy: 0.1072\n",
      "Epoch 288: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1200s 334ms/step - loss: 3.5588 - accuracy: 0.1072 - val_loss: 4.0859 - val_accuracy: 0.0876\n",
      "Epoch 289/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.4597 - accuracy: 0.1220\n",
      "Epoch 289: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1187s 331ms/step - loss: 3.4597 - accuracy: 0.1220 - val_loss: 11.9729 - val_accuracy: 0.0949\n",
      "Epoch 290/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.4123 - accuracy: 0.1258\n",
      "Epoch 290: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1230s 343ms/step - loss: 3.4123 - accuracy: 0.1258 - val_loss: 4.6478 - val_accuracy: 0.0541\n",
      "Epoch 291/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.7423 - accuracy: 0.0980\n",
      "Epoch 291: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1169s 326ms/step - loss: 3.7423 - accuracy: 0.0980 - val_loss: 4.5020 - val_accuracy: 0.0716\n",
      "Epoch 292/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.5580 - accuracy: 0.1086\n",
      "Epoch 292: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1192s 332ms/step - loss: 3.5580 - accuracy: 0.1086 - val_loss: 4.2894 - val_accuracy: 0.0783\n",
      "Epoch 293/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.6475 - accuracy: 0.1060\n",
      "Epoch 293: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1220s 340ms/step - loss: 3.6475 - accuracy: 0.1060 - val_loss: 4.2486 - val_accuracy: 0.0714\n",
      "Epoch 294/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.0987 - accuracy: 0.0795\n",
      "Epoch 294: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1175s 327ms/step - loss: 4.0987 - accuracy: 0.0795 - val_loss: 4.4827 - val_accuracy: 0.0587\n",
      "Epoch 295/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.0254 - accuracy: 0.0825\n",
      "Epoch 295: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1168s 325ms/step - loss: 4.0254 - accuracy: 0.0825 - val_loss: 4.5912 - val_accuracy: 0.0557\n",
      "Epoch 296/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.0974 - accuracy: 0.0794\n",
      "Epoch 296: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1167s 325ms/step - loss: 4.0974 - accuracy: 0.0794 - val_loss: 4.5428 - val_accuracy: 0.0712\n",
      "Epoch 297/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.0471 - accuracy: 0.0806\n",
      "Epoch 297: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1190s 332ms/step - loss: 4.0471 - accuracy: 0.0806 - val_loss: 4.6027 - val_accuracy: 0.0571\n",
      "Epoch 298/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.0283 - accuracy: 0.0812\n",
      "Epoch 298: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1125s 314ms/step - loss: 4.0283 - accuracy: 0.0812 - val_loss: 5.2916 - val_accuracy: 0.0550\n",
      "Epoch 299/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 4.1050 - accuracy: 0.0751\n",
      "Epoch 299: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1106s 308ms/step - loss: 4.1050 - accuracy: 0.0751 - val_loss: 4.9656 - val_accuracy: 0.0734\n",
      "Epoch 300/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.8335 - accuracy: 0.0914\n",
      "Epoch 300: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1152s 321ms/step - loss: 3.8335 - accuracy: 0.0914 - val_loss: 4.2909 - val_accuracy: 0.0806\n",
      "Epoch 301/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.9197 - accuracy: 0.0851\n",
      "Epoch 301: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1152s 321ms/step - loss: 3.9197 - accuracy: 0.0851 - val_loss: 4.9083 - val_accuracy: 0.0616\n",
      "Epoch 302/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.8795 - accuracy: 0.0884\n",
      "Epoch 302: val_accuracy did not improve from 0.50458\n",
      "3588/3588 [==============================] - 1138s 317ms/step - loss: 3.8795 - accuracy: 0.0884 - val_loss: 4.4502 - val_accuracy: 0.0762\n",
      "Epoch 303/3000\n",
      "3588/3588 [==============================] - ETA: 0s - loss: 3.7549 - accuracy: 0.0968\n",
      "Epoch 303: val_accuracy did not improve from 0.50458\n",
      "Restoring model weights from the end of the best epoch: 3.\n",
      "3588/3588 [==============================] - 1139s 317ms/step - loss: 3.7549 - accuracy: 0.0968 - val_loss: 4.4852 - val_accuracy: 0.0781\n",
      "Epoch 303: early stopping\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Compile the model\n",
    "# model = create_model_conv_with_dropout(input_shape = (None,x_train[20][0].shape[-1]), output_shape = y_train.shape)\n",
    "model = create_model_lstm_with_dropout(input_shape = (30,x_train[20][0].shape[-1]), output_shape = y_train.shape)\n",
    "optimizer = Adam(learning_rate=0.01)  # Adjust learning rate if needed\n",
    "model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Define callbacks\n",
    "checkpoint = ModelCheckpoint(\"best_model_weights_NN_LSTM+Dropout.h5\", monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', patience=300, verbose=1, mode='max', restore_best_weights=True)\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_train, y_train, epochs=3000, batch_size=512, validation_data=(x_val, y_val), callbacks=[checkpoint, early_stopping])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('Step 3_NN_LSTM+Dropout.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Step 3_NN_LSTM+Dropout.joblib']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model, 'Step 3_NN_LSTM+Dropout.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"Step 3_NN_LSTM+Dropout.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"Step 3_NN_LSTM+Dropout.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3022/3022 [==============================] - 49s 16ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.2590814e-15 4.1556122e-13 4.1148881e-14 ... 5.0789050e-13\n",
      "  1.0703996e-12 1.1647162e-13]\n",
      " [1.7595616e-07 5.3882521e-10 8.4704695e-08 ... 3.9089532e-10\n",
      "  1.2799381e-08 5.4664273e-10]\n",
      " [6.2456071e-09 3.7929041e-08 2.4726119e-09 ... 8.2269502e-10\n",
      "  4.7781441e-09 6.4073868e-10]\n",
      " ...\n",
      " [1.6942870e-09 5.6468186e-10 7.1814832e-10 ... 2.4140501e-13\n",
      "  7.9642021e-10 2.3635683e-13]\n",
      " [5.9279852e-11 3.0549085e-10 1.5207879e-10 ... 1.5259255e-09\n",
      "  1.6003836e-12 2.4207618e-09]\n",
      " [1.3842222e-06 3.7211007e-06 3.0882945e-06 ... 5.8503815e-09\n",
      "  2.0951837e-10 7.1487039e-09]]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Convert the list of lists to a numpy array\n",
    "data_array = np.array(y_pred)\n",
    "\n",
    "# Get the index of the maximum value in each list\n",
    "max_indices = np.argmax(data_array, axis=1)\n",
    "\n",
    "# Create a new array with the same shape as data_array, filled with zeros\n",
    "y_pred_result = np.zeros_like(data_array)\n",
    "\n",
    "# Set the maximum value indices to 1 in each row\n",
    "y_pred_result[np.arange(len(data_array)), max_indices] = 1\n",
    "\n",
    "print(y_pred_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_list_to_index(y):\n",
    "    # Convert the list of lists to a numpy array\n",
    "    data_array = np.array(y)\n",
    "\n",
    "    # Get the index of the maximum value in each list\n",
    "    max_indices = np.argmax(data_array, axis=1)\n",
    "\n",
    "    # Add 1 to each index to get index+1\n",
    "    index_plus_one = max_indices + 1\n",
    "\n",
    "    print(index_plus_one)\n",
    "\n",
    "    return index_plus_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[306 402  64 ... 135 132 154]\n",
      "[306 402  60 ... 131 132 154]\n"
     ]
    }
   ],
   "source": [
    "y_pred_mech = convert_list_to_index(y_pred_result)\n",
    "y_val_mech = convert_list_to_index(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data\n",
    "plt.scatter(y_val_mech, y_pred_mech, marker='o', s = 1, label='Data Points')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('y_val')\n",
    "plt.ylabel('y_val_predicted')\n",
    "plt.title('LSTM+Dropout')\n",
    "\n",
    "# # Adding a legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the percentage of correct predictions for each unique pair of true and predicted values\n",
    "count_dict = {} \n",
    "for true_val, pred_val in zip(y_val_mech, y_pred_mech):\n",
    "    if (true_val, pred_val) not in count_dict:\n",
    "        count_dict[(true_val, pred_val)] = 1\n",
    "    else:\n",
    "        count_dict[(true_val, pred_val)] += 1\n",
    "# print(count_dict)\n",
    "\n",
    "# Calculate total counts for each value starting from 1 to 456 for key[0]\n",
    "total_counts = {}\n",
    "for i in range(1, 457):\n",
    "    total_counts[i] = sum(value for key, value in count_dict.items() if key[0] == i)\n",
    "# print(total_counts)\n",
    "\n",
    "\n",
    "# Calculate percentages\n",
    "percentages = {}\n",
    "for key, value in count_dict.items():\n",
    "    # print(key)\n",
    "    # print(value)\n",
    "    percentages[key] = (value / total_counts[key[0]]) * 100\n",
    "# print(percentages)\n",
    "\n",
    "# Extract x and y data for the scatter plot\n",
    "x_data = [key[0] for key in percentages]\n",
    "y_data = [key[1] for key in percentages]\n",
    "colors = [value for value in percentages.values()]\n",
    "\n",
    "plt.figure(figsize=(11, 8))  # Adjust width and height as needed\n",
    "\n",
    "# Create the scatter plot\n",
    "# plt.scatter(x_data, y_data, c=colors, cmap='viridis', s = 1)\n",
    "plt.scatter(x_data, y_data, c=colors, cmap='Reds', s = 3) #, zorder=1)\n",
    "\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Label of True Mechanism', fontsize = 24, labelpad=15)\n",
    "plt.ylabel('Label of Predicted Mechanism', fontsize = 24, labelpad=15)\n",
    "# plt.title('Scatter Plot')\n",
    "\n",
    "# Add color bar\n",
    "cbar = plt.colorbar(ticks=[0, 20, 40, 60, 80, 100])\n",
    "cbar.set_label('Percentage', fontsize=18)  # Adjust the fontsize and labelpad as needed\n",
    "# Set font size of ticks in color bar\n",
    "cbar.ax.tick_params(labelsize=16)  # Adjust the font size as needed\n",
    "\n",
    "# Add axis ticks\n",
    "plt.xticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.yticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.tick_params(axis='both', direction='in')  # Set ticks to be inside the plot\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of matched values: 50.45821266032271\n"
     ]
    }
   ],
   "source": [
    "def calculate_matched_percentage(list1, list2):\n",
    "    # Ensure that both lists have the same length\n",
    "    if len(list1) != len(list2):\n",
    "        raise ValueError(\"Lists must have the same length\")\n",
    "\n",
    "    # Initialize a counter for matches\n",
    "    num_matches = 0\n",
    "\n",
    "    # Iterate over each pair of elements in the lists\n",
    "    for item1, item2 in zip(list1, list2):\n",
    "        # Check if the items match\n",
    "        if item1 == item2:\n",
    "            num_matches += 1\n",
    "\n",
    "    # Calculate the percentage of matched values\n",
    "    matched_percentage = (num_matches / len(list1)) * 100\n",
    "\n",
    "    return matched_percentage\n",
    "\n",
    "# Assuming y_pred_mech and y_val_mech are lists containing predicted and true values respectively\n",
    "matched_percentage = calculate_matched_percentage(y_pred_mech, y_val_mech)\n",
    "print(\"Percentage of matched values:\", matched_percentage)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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